Toni SzymanskiRelated papers
A profile of motivation, self-perception, and achievement in black urban elementary students
The Urban Review, 1991
The purpose of this study was to develop a profile of a sample of black urban elementary students based on motivational tendencies, self-perceptions, and achievement. The article explores the intrinsic motivation and self-perceptions of these students and seeks to find relationships among these qualities and academic achievement and attendance. The results of the study present a profile that reflects a lack of social acceptance of vocabulary skills and a lack of social acceptance of preferring challenging work. Perception of scholastic competence was related to behavioral conduct but not to global self-worth. The results supported previous research indicating that some black students perceive schooling and academic success as inconsistent with their racial-cultural goals. Implications for urban schools are presented.
Fostering Higher Academic Performance in African American Youth Through Enhanced Self-Efficacy: The Importance of Integrated Racial-Ethnic Identity
Journal of Higher Education Theory and Practice, 2021
Although the dimensions that constitute racial-ethnic identity (REI) interrelate to affect psychosocial and academic outcomes, few studies have explored the nature (e.g., directionality) of these interrelations in understanding a strong and healthy REI among African American youth in relation to psychosocial and academic outcomes. In the current study, we applied path analysis to investigate the potential mediation effects of private regard in translating the effects of racial centrality and public regard to affect African American youth's academic performance through self-efficacy. Using a nationally representative sample of African American youth, findings suggest that racial centrality and public regard correlate to affect private regard, controlling for neighborhood safety and sociodemographic backgrounds. Private regard in turn, associated with self-efficacy to affect academic performance measured by GPA. These results indicate the need to investigate the potential mediation effects among REI dimensions to understand the nuanced pathways REI dimensions configure to form a strong and healthy REI to affect psychosocial and academic outcomes among African American youth.
Academic Identity: A Longitudinal Investigation of African American Adolescents’ Academic Persistence
Journal of Black Psychology, 2016
Adolescence is a vulnerable period for the development of academic identification and academic persistence, particularly among African American adolescents. The present study investigated how cultural assets (i.e., private regard and racial centrality) and academic assets (i.e., academic curiosity and academic self-esteem) influence African American adolescent boys’ ( n = 109) and girls’ ( n = 153) academic persistence over time. Additionally, we explored whether oppositional academic identity mediated the relationships between academic and cultural assets and academic persistence. Data were drawn from a cross-sectional longitudinal study. Results indicated significant direct effects of academic assets on academic persistence at Times 1 and 2 for boys and at Times 1, 2, and 3 for girls. Furthermore, oppositional academic identity mediated the relationship between boys’, but not girls’, cultural assets and academic persistence at Time 1. These findings have implications for understan...
Racial Identity and Academic Attainment Among African American Adolescents
Child Development, 2003
In this study, the relationships between racial identity and academic outcomes for African American adolescents were explored. In examining race beliefs, the study differentiated among (a) importance of race (centrality), (b) group affect (private regard), and (c) perceptions of societal beliefs (public regard) among 606 African American 17-year-old adolescents. Using cluster analysis, profiles of racial identity variables were created, and these profile groups were related to educational beliefs, performance, and later attainment (high school completion and college attendance). Results indicated cluster differences across study outcomes. Also, the relationships between academic attitudes and academic attainment differed across groups. Finally, the paper includes a discussion on the need to consider variation in how minority youth think about group membership in better understanding their academic development.
The Role of Racial Identity, Academic Self-Concept, and Self-Esteem in the Prediction of Academic Outcomes for African American Students
Journal of Black Psychology, 2007
The purpose of the present study was to examine the extent to which racial identity, academic self-concept, and self-esteem predict two types of academic outcomes, grade point average (GPA), and verbal Graduate Record Examination scores. Although grades and standardized test performance are often collapsed under the category of academic performance, no studies have examined both of these variables' relations with racial identity, academic self-concept, and self-esteem together in the same study. A total of 313 African American students derived from two separate samples participated in the current study. The results indicated that academic self-concept significantly predicted students' GPA but not their test scores. Furthermore, racial identity did not predict either grades or test performance. The strongest predictor of test performance was the age of the student. Implications of the study findings are discussed.
Can racial identity be promotive of academic efficacy?
International Journal of Behavioral Development, 2001
Seeing the Destination AND the Path: Using Identity-Based Motivation to Understand and Reduce Racial Disparities in Academic Achievement
African Americans, Latinos, and Native Americans aspire to do well in school but often fall short of this goal. We use identity-based motivation theory as an organizing framework to understand how macrolevel social stratification factors including minority–ethnic group membership and low socioeconomic position (e.g., parental education, income) and the stigma they carry, matter. Macrolevel social stratification differentially exposes students to contexts in which choice and control are limited and stigma is evoked, shaping identity-based motivation in three ways. Stratification influences which behaviors likely feel congruent with important identities, undermines belief that one's actions and effort matter, and skews chronic interpretation of one's experienced difficulties with schoolwork from interpreting experienced difficulty as implying importance (e.g., " it's for me ") toward implying " impossibility. " Because minority students have high aspirations , policies should invest in destigmatizing, scalable, universal, identity-based motivation-bolstering institutions and interventions.
Factors that contribute to the academic success of African American males: Perceptions of African American male high school students
2013
Much of the literature dedicated to the academic achievement o f African American males focuses on failure, obstacles, negative influences and explanations of factors that negatively impact their academic success. This qualitative research study provided an opportunity for African American male students at the high school level to articulate their experiences and speak to the factors that they perceived as contributing to their academic success. The constructs of identity and cultural capital were offered by this researcher as a conceptual framework into the insight o f factors that impacted the academic achievement of this student group. Through interviews, a classroom observation and document review, the perceptions of six African American male seniors from two public high schools located in southeastern Virginia were collected and analyzed. The themes derived from the data showed that these young men were successful due to the support they received from their teachers, parents and peers, their approach to challenging and difficult situations, and the opportunities that they were afforded that led to their use of the educational process to reach their goals and dreams. FACTORS THAT CONTRIBUTE TO THE ACADEMIC SUCCESS OF AFRICAN AMERICAN MALES: PERCEPTIONS OF AFRICAN AMERICAN MALE HIGH SCHOOL STUDENTS xiv Chapter 1: Statement of the Problem Historically, minority populations across the United States and the world have dealt with inequities that have included the denial of the right to vote, to own property, to be meaningfully employed, and to pursue an education. In the United States, many of these rights were restored through landmark cases such as Brown v. Board o f Education and the passage of the Civil Rights Act. Davis (2005) and Monroe (2005 a & b) suggested that there remains a residual effect of these inequities, which can be seen in education as manifested in issues of achievement, drop-out rates, discipline practices, over representation of minority student groups in categories such as special education, and under-representation in areas such as gifted and talented. Data provided by the National Assessment of Educational Progress (NAEP) for the state of Virginia illustrates the issue of inequities for African American males (Holzman, 2010). Data indicate that there are approximately 162,679 black males enrolled in K-12 programs in Virginia. The 2007-2008 graduation rate for African American males was 49% while the graduation rate for White males was found to be 73% (Holzman, 2010). The data revealed that out-of school-suspension rates for Black males was equivalent to 18% of Virginia's enrollment of African American males while White male students were suspended at a rate equivalent to seven percent of their enrollment. The data also indicated that African American males were expelled at a proportion that was twice that of their enrollment. While significant numbers o f African American males are removed
Racial Differences in Eighth Grade Students’ Identification With Academics
Education and Urban Society, 2010
Having a positive attitude about school, valuing school, and being intrinsically motivated is associated with higher grades, graduation, and college attendance. However, the literature reveals that these characteristics and attitudes are not uniform across race, class, and gender. The purpose of the current study was to examine racial differences in student identification with middle school. Specifically the study looked at the ways in which African American and White students described abstract and concrete components of identification. A total of 256 African American and 208 White eighth grade students from four middle schools in the Midwest completed a 13-item Identification with Academics survey as part of a large study of the transition from middle school to high school. Results show that African American students held higher abstract identification with academics than White students. Step-wise linear regression analysis demonstrates that the 74 Education and Urban Society 43(1) abstract identification is associated with academic achievement controlling for demographic characteristics. The findings of the study have implications for developing and assessing programs that help students to think about their identification with academics.
University of Iowa
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Iowa Research Online
Theses and Dissertations
Summer 2011
Pathways toward progress: examining the
relationships among racial identity, academic
intrinsic motivation, and perceived suppport on
African American students' academic achievement
Antonia Maria Kennedy Szymanski
University of Iowa
Copyright 2011 Antonia Maria Kennedy Szymanski
This dissertation is available at Iowa Research Online: https://ir.uiowa.edu/etd/1270
Recommended Citation
Szymanski, Antonia Maria Kennedy. "Pathways toward progress: examining the relationships among racial identity, academic intrinsic
motivation, and perceived suppport on African American students' academic achievement." PhD (Doctor of Philosophy) thesis,
University of Iowa, 2011.
https://doi.org/10.17077/etd.1d1q7wou
Follow this and additional works at: https://ir.uiowa.edu/etd
Part of the Educational Psychology Commons
PATHWAYS TOWARD PROGRESS: EXAMINING THE RELATIONSHIPS
AMONG RACIAL IDENTITY, ACADEMIC INTRINSIC MOTIVATION, AND
PERCEIVED SCHOOL SUPPORT ON AFRICAN AMERICAN STUDENTS'
ACADEMIC ACHIEVMENT
by
Antonia Maria Kennedy Szymanski
An Abstract
Of a thesis submitted in partial fulfillment
of the requirements for the Doctor of
Philosophy degree in Psychological and Quantitative Foundations
in the Graduate College of
The University of Iowa
July 2011
Thesis Supervisors: Professor David Lohman
Assistant Professor Malik Henfield
1
Academic as achievement reflects the accumulation over time of interactions
between a student and the demands and affordances of the situations in which they
attempt to learn particular academic knowledge and skills. Aptitude theory describes the
cognitive, affective, and conative variables that moderate the success these interactions.
Racial identity may be an essential aptitude variable for explaining academic
achievement for non-white students. Black racial identity is described as the importance
and understanding that African American students ascribe to belonging to a Black racial
group in a society that is dominated by non-Black members. Investigating how the factors
of racial identity (private regard, public regard, and racial centrality) are associated with
the other key aspects of performance may guide efforts to support African American
students in improving their academic performance.
The purpose of this study was to investigate relationships among racial identity,
academic intrinsic motivation, perceived school support, and academic achievement for
African American students. Participants were students (N=56) in grades 11 and 12 from
two schools in a single Midwestern metropolitan district. Students completed
questionnaires regarding their perceptions of school support, academic intrinsic
motivation, and racial identity. The school provided information on current grade point
average, number of advanced placement courses taken, and sixth grade standardized
achievement test scores. Hierarchical linear regression models were used to investigate
whether the racial identity factors moderated the relationships between academic intrinsic
motivation and perceived school support on either grade point average or the number of
advanced placement courses taken, controlling for sixth-grade academic achievement.
2
Significant interactions were found between racial centrality and perceived school
support for academic intrinsic motivation. Significant interactions were also found for
racial centrality and private regard moderating the relationship between academic
intrinsic motivation and the number of advanced placement courses taken, controlling for
prior academic achievement. These findings suggest that racial centrality and private
regard may be important moderators of academic success for students with lower levels
of academic intrinsic motivation and lower perceived school support. These students may
benefit from interventions that support feeling positive about being Black and recognize
the importance of race in students’ self-concepts.
Abstract Approved: ____________________________________
Thesis Supervisor
____________________________________
Title and Department
____________________________________
Date
____________________________________
Thesis Supervisor
____________________________________
Title and Department
____________________________________
Date
PATHWAYS TOWARD PROGRESS: EXAMINING THE RELATIONSHIPS
AMONG RACIAL IDENTITY, ACADEMIC INTRINSIC MOTIVATION, AND
PERCEIVED SCHOOL SUPPORT ON AFRICAN AMERICAN STUDENTS’
ACADEMIC ACHIEVEMENT
by
Antonia Maria Kennedy Szymanski
A thesis submitted in partial fulfillment
of the requirements for the Doctor of
Philosophy degree in Psychological and Quantitative Foundations
in the Graduate College of
The University of Iowa
July 2011
Thesis Supervisors: Professor David Lohman
Assistant Professor Malik Henfield
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
_______________________
PH.D. THESIS
_______________
This is to certify that the Ph.D. thesis of
Antonia Maria Kennedy Szymanski
has been approved by the Examining Committee
for the thesis requirement for the Doctor of Philosophy
degree in Psychological and Quantitative Foundations at the July 2011
graduation.
Thesis Committee: ___________________________________
David Lohman, Thesis Co-Supervisor
___________________________________
Malik Henfield, Thesis Co-Supervisor
___________________________________
Robert Ankenmann
___________________________________
Megan Foley-Nicpon
___________________________________
Laurie Croft
To My Family, without you I would never have had the courage to go down this path.
You are the reason for everything I do.
ii
ACKNOWLEDGMENTS
I would like to thank Dr. Croft for her incredible support of gifted education and
those who want to help. Dr. Croft was the first person who encouraged me to pursue a
PhD in Educational Psychology which led me to Iowa when she didn’t even know me.
Her support and friendship helped me complete this journey when there were many times
I would have quit. Thank you also to Dr. Malik Henfield who offered support and
mentoring before I had even completed comps. Deepest thanks go to Dr. Ankenmann
who spent an entire summer working on chapter three with me. I could not have gotten
through this process without Dr. Ankenmann’s expertise and patience. I would like to
thank my advisor David Lohman for his patience and support throughout my PhD
process. Thank you also to Dr. Foley-Nicpon for her willingness to add yet another
commitment to her already incredible schedule. Dr. Coohey, School of Social Work was
integral in the conceptualization of my dissertation questions and gave tremendous
support in the development of my proposal.
As always, my family, Chris, Amanda, and Tristan are my real heroes who help
me so much. Thank you for drying my tears, believing in me when I didn’t, and your
willingness to give up everything for my chance to fulfill my dreams. If it weren’t for
you, I could have never done this. Thank you to my mom and extended family for your
love, support, and understanding that I had to move to find space to grow and to Char
who gives me courage everyday to live life as best I can.
iii
TABLE OF CONTENTS
LIST OF TABLES .................................................................................................................... vi
LIST OF FIGURES ................................................................................................................. viii
CHAPTER 1 INTRODUCTION................................................................................................. 1
Racial Identity ...................................................................................................................... 4
Motivation ............................................................................................................................ 6
School Climate and Teacher Relations .................................................................................. 8
Intelligence ........................................................................................................................... 8
Research Questions ............................................................................................................... 9
Importance and Relevance for the Field of Education ......................................................... 11
CHAPTER 2 LITERATURE REVIEW .................................................................................... 13
Academic Achievement ...................................................................................................... 13
Aptitude Theory.................................................................................................................. 15
Cognitive Ability ................................................................................................................ 17
Racial Identity .................................................................................................................... 22
Intrinsic Motivation ............................................................................................................ 35
School Support ................................................................................................................... 45
Summary ............................................................................................................................ 55
CHAPTER 3 METHODS ......................................................................................................... 58
Participants and Sampling ................................................................................................... 58
Determination of the Sample ............................................................................................... 59
IRB and Plan to Maintain Confidentiality ........................................................................... 60
Instruments ......................................................................................................................... 61
Data Analysis ..................................................................................................................... 65
CHAPTER 4 RESULTS ........................................................................................................... 70
Missing Data....................................................................................................................... 70
Preliminary Statistical Analyses .......................................................................................... 71
Statistical Analyses Used to Answer the Primary Research Questions ................................. 73
CHAPTER 5 CONCLUSIONS AND DISCUSSION ................................................................ 90
Conclusions ........................................................................................................................ 90
Discussion .......................................................................................................................... 91
Study Limitations ............................................................................................................... 94
Recommendations for Further Research .............................................................................. 96
REFERENCES ......................................................................................................................... 98
iv
APPENDIX A LETTER OF INFORMED CONSENT ........................................................... 108
APPENDIX B DEMOGRAPHIC QUESTIONNAIRE ........................................................... 112
APPENDIX C PERCEIVED SCHOOL SUPPORT QUESTIONNAIRE ................................ 114
APPENDIX D MULTIDIMENSIONAL INVENTORY OF BLACK IDENTITY .................. 116
APPENDIX E ACADEMIC MOTIVATION QUESTIONNAIRE .......................................... 118
APPENDIX F TABLES ......................................................................................................... 119
v
LIST OF TABLES
TABLE
3.1.
Key Demographics of Schools Participating in the Study .............................................. 58
F1.
Summary Statistics of Sample Recruitment District .................................................... 119
F2.
Student Demographic Data.......................................................................................... 120
F3.
Chi-Squared Analysis of Demographic Data ............................................................... 121
F4.
Summary Statistics of Outcome and Control Measures ............................................... 122
F5.
Summary Statistics of Scores on Self-Report Measures ............................................... 122
F6.
Correlations Between Scores on Self-Report Measures ............................................... 123
F7.
Regression Model Summary for Relationships Among Perceived Support (SUP),
Racial Identity Factors (CEN, PRI, PUB) and Academic Intrinsic Motivation
(MOT) ........................................................................................................................ 124
F8.
Regression and Correlation Coefficients for Relationships Among Perceived
Support (SUP), Racial Identity Factors (CEN, PRI, PUB) and Academic Intrinsic
Motivation (MOT) ...................................................................................................... 125
F9.
Regression Model Summary for Relationships Among Perceived Support (SUP),
Racial Identity Factors (CEN, PRI, PUB) and Academic Intrinsic Motivation –
Reduced Models ......................................................................................................... 126
F10.
Regression and Correlation Coefficients for Relationships Among Perceived
Support (SUP), Racial Identity Factors (CEN, PRI, PUB), and Academic Intrinsic
Motivation (MOT) – Reduced Models (A, B, and C) ................................................... 128
F11.
Regression Model Summary for Relationships Among Sixth Grade Academic
Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity
Factors (CEN, PRI, PUB), and Grade Point Average (GPA) ....................................... 131
F12.
Regression and Correlation Coefficients for Relationships Among Sixth Grade
Academic Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial
Identity Factors (CEN, PRI, PUB), and Grade Point Average (GPA)........................... 132
F13.
Regression Model Summary for Relationships Among Sixth Grade Academic
Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity
Factors (CEN, PRI, PUB), and Number of Advanced Placement Courses (NAP) ........ 133
F14.
Regression and Correlation Coefficients for Relationships Among Sixth Grade
Academic Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial
Identity Factors (CEN, PRI, PUB), and Number of Advanced Placement Courses
(NAP) ......................................................................................................................... 134
vi
F15.
Regression Model Summary for Relationships Among Sixth Grade Academic
Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity
Factors (CEN, PRI, PUB), and Number of Advanced Placement Courses (NAP) –
Reduced Models (A, B, and C).................................................................................... 135
F16.
Regression and Correlation Coefficients for Relationships Among Sixth Grade
Academic Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial
Identity Factors (CEN, PRI, PUB), and Number of Advanced Placement Courses
(NAP) – Reduced Models (A, B, and C)...................................................................... 137
F17.
Regression Model Summary for Relationships Among Sixth Grade Academic
Achievement (6TH), Perceived Academic Support (SUP), Racial Identity Factors
(CEN, PRI, PUB), and Grade Point Average (GPA) .................................................... 140
F18.
Regression and Correlation Coefficients for Relationships Among Sixth Grade
Academic Achievement (6TH), Perceived Academic Support (SUP), Racial
Identity Factors (CEN, PRI, PUB), and Grade Point Average (GPA)........................... 141
F19.
Regression Model Summary for Relationships Among Sixth Grade Academic
Achievement (6TH), Perceived Academic Support (SUP), Racial Identity Factors
(CEN, PRI, PUB), and Number of Advanced Placement Courses (NAP)..................... 142
F20.
Regression and Correlation Coefficients for Relationships Among Sixth Grade
Academic Achievement (6TH), Perceived Academic Support (SUP), Racial
Identity Factors (CEN, PRI, PUB), and Number of Advanced Placement Courses
(NAP) ......................................................................................................................... 143
vii
LIST OF FIGURES
FIGURE
4.1.
Graph of Interaction Between Private Regard and Perceived Support on Academic
Motivation .................................................................................................................... 77
4.2.
Graph of Interaction Between Centrality and Motivation on Number of Advanced
Placement Courses ........................................................................................................ 82
4.3.
Graph of Interaction Between Private Regard and Motivation on Number of
Advanced Placement Courses ....................................................................................... 83
viii
1
CHAPTER 1 INTRODUCTION
The election of the first African American president of the United States may give
the impression that African American students receive the education and support that
they need to develop their potential. However, research shows the opposite is true.
African American students are disproportionately overrepresented in remedial classes and
underrepresented in programs for gifted students (Skiba, Poloni-Staudinger, Gallini,
Simmons, & Feggins-Azziz, 2006; Elhoweris, Mutua, Alsheikh, Hollowya, 2005; Ferri &
Connor, 2005; Harris III, Brown, Ford, & Richardson, 2004; Patton, 1998). Studies show
that the underrepresentation in gifted programs is so substantial that it has been called,
―The most segregated portion of education in this nation.‖(Ford, 1995, p. 52). A possible
explanation for this disproportionate representation may be that African American
students behave differently from European American students in the classroom due to
differences in motivation and perceived social context. Barton and Coley (2010), report
that the NAEP achievement gap in reading and mathematics has not diminished since
1980 with the exception of the reading gap for 9-year-old students. The goal of this
research is to build upon current conceptualizations of the factors that affect academic
achievement for African American students. Information from this study may help
teachers, administrators and parents understand the unique needs of African American
students and thereby create interventions that may aid the development of these learners.
Colangelo, Kerr, Maxey and Christensen (1992) noted that 0.8% of students who
scored at the 95th percentile on the ACT were African American. This dismal state of
academic achievement for African American students has not shown much improvement
2
over the past 20 years. ACT reports that only 35% of all African American students who
took the ACT in 2009 met the benchmark score (50% chance of obtaining a B or higher,
or a 75% chance of obtaining a C or higher in corresponding college course work; ACT,
2010). In 2009, the average ACT composite score of White students was 23.0, 1.1
standard deviations higher than the average African American score of 17.6. This
difference provides evidence that an achievement gap persists between academically
advanced Black and White students. Higher scores provide opportunities afforded by of
scholarships and admission to prestigious colleges. Elementary and secondary students
need support, encouragement, and appropriate academic preparation to take advanced
coursework that will lay the foundation for post-secondary studies.
Better understanding of the psychosocial foundations of academic learning could
enable educators to develop better methods for assisting students to maximize their
academic achievement. Academic achievement, as estimated by scores on standardized
tests and grade-point average, has always been the primary concern of those interested in
education. The achievement disparity between Black and White students led to the
passage of Federal mandates that aim to increase achievement for African American
students such as the No Child Left Behind Act of 2001. Although some progress has been
made such as the decrease in reading score differences on NAEP tests, the achievement
gap between African American and White students is still substantial (Barton & Coley,
2010). Even small group differences at the mean of a distribution may translate into large
group differences at the tails of the distribution. Recently, researchers have drawn
attention to the large group differences at the upper end of the performance distribution
(Plucker, Burroughs, & Song 2009; Loveless, 2009). One of the findings in advanced
3
mathematics has been that the gaps between Black and White students have increased
from 1996 to 2007 ranging from 6.8% - 8.5%. Another finding was that Grade 4 reading
showed the percentage of White students scoring at advanced levels increased by 1.4
percentage points whereas Black students scoring at advanced levels increased by 0.7%
(Plucker, Burroughs, & Song, 2009).
Various theories have been developed to explain differences between Black and
White students on standardized measures of performance such as NAEP and ACT tests.
Most modern theories view the scores as reflecting the accumulated influence of many
different factors operating over the years that children prepare for and participate in the
educational system (Corno, Cronbach, Kupermintz, Lohman, Mandinach, Porteus &
Talbert, 2002; Snow, 1992). Some researchers argue that ―stereotype threat‖ creates
increased anxiety on African American students which reduces their test performance
(Steele, & Aronoson, 1995). Stereotype threat, which has been defined as ―being at risk
of confirming as self-characteristic, a negative stereotype about one’s group‖ (Steele &
Arononson, 1995, p.797), has been found to produce additional anxiety for members of
the marginalized group. The anxiety of anticipating a negative interaction or a feeling of
having to represent a race of people may impair performance. Fordham and Ogbu (1986)
claim that academic achievement is seen as a ―White‖ domain and a refusal to participate
in school is a rejection of White culture. Other researchers point to the heritability of
general intelligence with lower IQ group means as indicative of genetic restrictions on
intelligence leading to poor academic performance (Jensen, 1998). Since the inception of
intelligence testing, theories have existed to explain intelligence and individual variation
4
of performance. It is important to understand the various dimensions of intelligence in
order to understand individual differences in academic performance.
Racial Identity
Ethnic or racial identity theories may help explain the lower achievement of
African American students. Worrell (2009) suggests that education is not merely an
individual experience but is situated in a social context and is influenced by one’s self
perception. Racial identity, while not often recognized by the majority culture, is a major
factor in the creation of self-perception of members of subgroups of the population.
Situations are interpreted in part through the perceptions created by racial identity and
thus may be interpreted differently by members of the minority subgroups (Grantham &
Ford, 2003; Cokley, 2001).
One of the problems with much research on the impact of racial identity on
academic achievement in African American students is that it takes a unidimensional
view of both constructs. Researchers hold that there is a direct, linear relationship
between how a student views his or her racial identity and academic achievement though
results of investigations have been mixed. However, years of research have shown that
academic achievement is a multidimensional outcome that reflects the influence of a
person’s cognitive ability, his or her level of motivation, affective characteristics, and the
situation. Reducing the explanation to two unidimensional variables, racial identity and
academic achievement, may miss important information. It is the combinations of
contributions from different facets of racial identity to different aspects of achievement
that have been ignored which may resolve the inconsistencies in the findings and provide
valuable insights for researchers.
5
Black racial identity has been described by Sellers, Smith, Shelton, Rowley, and
Chavous as ―the significance and qualitative meaning that individuals attribute to their
membership within the Black racial group within their self-concepts‖ (1998, p. 23).
Academic institutions and classrooms are typically led by Caucasians. As such, school
may represent the dominant culture to racially diverse student. The perceptions regarding
the importance of race, how the public perceives African American students, and how the
individual feels about being identified as belonging to a particular racial group, may
influence interactions between the individual, peers, and teachers thus impacting the
academic experience.
The Multi-dimensional Model of Racial Identity holds that identity is multifaceted and situational (Sellers, et al., 1998). Different situations may highlight various
identities of individuals. Racial identity research seeks to understand the role of race in
individual’s interpretation and response in social situations. Components of racial identity
such as centrality and regard have been demonstrated to be stable within the individual.
Understanding how these components may operate differently for people and be
manifested in different behaviors may help understand individual differences in response
to interventions. Furthermore, understanding that both racial identity and academic
achievement have multiple dimensions allows researchers to explore the more nuanced
relationships between the dimensions of each that may be missed by using overall general
measures.
Some researchers assert that in situations where racial differences are highlighted,
(e.g. one minority in a group of majority race people) that some African Americans may
feel anxiety due to their perceptions regarding majority cultures’ existing stereotypes
6
(Steele & Aronson, 1995). The situation may increase the centrality of race in the
individual’s identity because of a feeling of being different from the rest of the group. If
the individual holds a negative perception of public regard, it could influence
performance because anxiety regarding Caucasian expectations may be increased if
individuals feel that they have to disprove negative stereotypes or serve as representatives
for an entire race.
Other researchers contend that negative perceptions of public regard may serve as
a catalyst that leads individuals to reject the dominant culture (Fordham & Ogbu, 1986).
In an academic setting, school may be seen as the dominant culture and academic
achievement may be rejected as a means of coping with prior negative experiences. A
person for whom race is important in defining who they are and who has experienced the
negative stereotypes that may be held by majority culture may feel compelled to reject
White culture as a means of demonstrating racial solidarity.
Examining academic performance through the lens of racial identity may provide
new insights. Refining racial identity into components of racial centrality – the
importance of race in who a person is, perceived public regard, and perceived private
regard allows a more nuanced understanding regarding which component(s) interact with
other performance variables. This level of understanding has been lacking in other
research regarding the role of identifying with a particular race and its relationship with
academic achievement.
Motivation
Many theorists distinguish between internal and external sources of motivation.
For example, Ryan and Deci (2004) describe intrinsic motivation as an internal drive that
7
compels individuals to engage in specific behavior such as learning. Extrinsic motivation,
on the other hand, is the result of external sources that influence behavior, often through
the use of threats and rewards. Both sources of motivation may influence academic
achievement by reinforcing behaviors that increase student success.
Individuals may differ in their sources and levels of motivation. Findings
regarding motivation of African American students have been mixed. Some researchers
found that African American students seem to be more externally motivated than White
students. However, this does not lead to negative performance consequences (Cokley,
2003; Cokley, 2002; Graham, 1994). Conversely, Shernoff and Schmidt (2008) found
that African American students had higher intrinsic motivation and affect scores than
White students even when their achievement was lower. The study also showed a
negative interaction between engagement and grades for African American students (ES
= -.20). Further investigation is warranted to understand how motivational components
may operate differently for African Americans.
Individuals vary in their beliefs regarding the extent to which they view cognitive
ability as fixed or malleable which may affect motivation. Beliefs regarding cognitive
ability may also affect academic achievement. Researchers have found that individuals
who hold incremental beliefs regarding cognitive ability show higher persistence for
challenging tasks and are willing to attempt more difficult tasks than individuals who
hold entity beliefs regarding ability (Blackwell, Trzesniewski, & Dweck, 2007; Spray,
Wang, Biddle, Chatzisarantis, & Warburton, 2006; Cury, Elliot, DaFonseca, & Moller,
2006; Gottfried, Gottfried, Cook & Morris, 2005). Cury, Elliot, DaFonseca and Moller
(2006) found that students who held entity beliefs tended to focus on performance and
8
performance-avoidance goals depending on their personal evaluations of mathematical
ability. Pepi, Faria and Alesi (2006) found significant interactions between cultural
background and education level of parents in predicting whether an individual adopts a
fixed or malleable view of cognitive ability.
School Climate and Teacher Relations
Students’ perceptions of teacher relations and school climate have also been
shown to affect the motivation and self-concept of African American students (Ford,
Grantham, & Whiting, 2008; Milner & Ford, 2005). Stereotype threat has been shown to
extend beyond testing conditions (Steele, 1997). Students may perceive teachers’ lower
expectations and respond in ways which fulfill the expectations at very young ages. Thus,
the students who show the potential for advanced achievement may be influenced
positively or negatively by their experiences with teachers and the teachers’ expectations.
Worrell (2009) has indicated a need for further research to identify whether various
identity profiles have unique consequences for learning.
Intelligence
Theories of intelligence recognize that intelligent behavior is a combination of
developed cognitive abilities and other characteristics such as personality, motivation,
and creativity (Sternberg, 2002; Ackerman, 1996; Wechsler, 1975; Cattell, 1943). By
examining these clusters of characteristics as they are used in the situation in which an
individual is asked to demonstrate intelligence, more of the variability in performance
may be explained than a static measure of cognitive ability alone (Snow, 1992). A similar
breadth of characteristics is commonly recognized among those who study giftedness.
For example, Renzulli’s Triarchic Model contends that giftedness may be conceptualized
9
as three overlapping characteristics: cognitive ability, motivation, and creativity
(Renzulli, 1977). For African American students, racial identity may be an important
factor in understanding the unique issues faced by students of color. Racial identity may
impact demonstrations of cognitive ability, affective characteristics, or motivation.
Research Questions
The purpose of this study is to understand the relationships among components of
racial identity, academic intrinsic motivation, perceived school support, and prior
academic achievement in predicting academic achievement for African American
students. More specifically, this study will focus on the influence of racial identity on
motivation, perception of school climate, and current academic achievement. Research to
date has yielded mixed results on the relationship of racial identity and achievement. Part
of the discrepancies observed in previous studies may be a result of measures of racial
identity (Sellers, Rowley, Chavous, Shelton, & Smith, 1997). These inconsistencies may
reflect the fact that studies have used various measures of racial identity, each of which
emphasizes a somewhat different aspect of the construct. Using a measure of racial
identity with three components will allow a more detailed examination of the variables
that interact with the components of individual differences in academic achievement.
Several research areas will be examined. First, the linear relationship between the
three components of racial identity (centrality, public regard, and private regard) in
Seller’s Multidimensional Inventory of Black Identity and two components of academic
motivation (intrinsic motivation, and perceived school support) among 11 th and 12th grade
African American students will be investigated. Second, they ways in which the three
factors of racial identity (centrality, public regard or private regard) singly or in
10
combination moderate the observed relationships between perceived academic support
and intrinsic academic motivation will be explored. Third, an understanding of the role of
each racial identity factor and whether it moderates a relationship between academic
intrinsic motivation and academic achievement and between perceived school support
and academic achievement will be sought. Accordingly, the research questions are:
1. What are the relationships between the set of racial identity factors (centrality,
public regard, and private regard) and perceived support?
2. What are the relationships between the set of racial identity factors (centrality,
public regard, and private regard) and intrinsic motivation?
3. Do racial identity factors moderate the relationship between perceived support
and motivation?
4. Do racial identity factors (centrality, public regard, and private regard) moderate
the relationship between academic intrinsic motivation and cumulative grade
point average after controlling for 6th grade standardized test scores?
5. Do the racial identity factors (centrality, public regard, and private regard)
moderate the relationship between academic intrinsic motivation and the number
of advanced placement courses taken after controlling for 6th grade standardized
test scores?
6. Do racial identity factors (centrality, public regard, and private regard) moderate
the relationship between perceived school support and cumulative grade point
average after controlling for 6th grade standardized test scores?
7. Do the racial identity factors (centrality, public regard, and private regard)
moderate the relationship between perceived school support and the number of
advanced placement courses taken after controlling for 6th grade standardized test
scores?
11
Importance and Relevance for the Field of Education
Studies to date have yielded mixed results when analyzing the relationship
between racial identity and achievement. The planned analysis will add information to
the understanding of this relationship by examining important variables that influence
academic achievement in high-school in conjunction with racial identity. For example,
students’ scores on reports of motivation will be used in conjunction with their reports of
racial identity components to explain variance in grade point average. The students will
also be compared with one another on their racial identity, intrinsic motivation, and
perceived school support. By understanding similarities and differences between higher
and lower academic achievers we may find areas that schools and teachers can strengthen
to increase achievement.
Most research on African American academic achievement focuses on differences
between Black and White students. The tendency, then, is to adopt a deficit view in order
to explain the problem that is preventing African American students from achieving.
While solving problems is the nature of most research, this view may reinforce the
cultural deficit model that may be in place systematically. Therefore, this research is
focused on exploring relationships among racial identity factors, perceived school
support, cognitive ability and motivation for African American students in an effort to
illuminate those areas of strength that can be strengthened and supported by teachers and
administrators. Researchers have learned a great deal regarding differences of the two
groups; however, the time has come to focus on within-group variability.
This study will add to the literature on intelligence theory and research by
including racial identity as a key component in explaining achievement. The results of
12
this research may also be used by policy makers and administrators to create programs
and environments that will support academic achievement for African American students.
Organization
This dissertation is organized into five chapters. Chapter one is an overview of the
research problem and brief background explanation. Chapter two is a review of the
literature on the relationship between achievement, cognitive ability, motivation, racial
identity, and perceived school support for African American adolescents. The literature
review begins with an overview of aptitude and academic achievement. The review then
focuses on (a) cognitive ability as it relates to academic achievement, (b) racial identity
formation and the impact of racial identity on academic achievement, (c) the effect of
motivation on academic achievement, and (d) perceived social context or school support
effects on academic achievement for all ethnic racial groups, and student perceptions of
teacher expectations on academic achievement. The literature review ends with a
summary of the state of the literature and a clear explanation of how the dissertation
research adds to the current body of knowledge. Chapter three explains the methods that
are used to carry out the proposed analysis. Chapter four provides the results of the data
analysis. Chapter five discusses the results of the data and implications of the research.
13
CHAPTER 2 LITERATURE REVIEW
This chapter will review the relevant literature in four areas: racial identity
development for African Americans, motivation, teacher support and school climate, and
academic achievement. The final section of the chapter explores relations among these
factors and summarizes the research questions of the proposed study.
Academic Achievement
Educational achievement is a cumulative product of classroom interactions that
begin in elementary school. Achievement is typically estimated by grade point average
(GPA), scores on standardized achievement tests, or completion of a course of study.
Other measures of academic achievement may include participation in Advanced
placement or honors courses in high school. Low academic achievement in the K-12
setting limits the educational choices for students upon graduation. Academic
achievement influences future earnings. Not surprisingly, more education results in
higher income. The National Center for Education Statistics reports that adults, aged 2534, who earn a bachelor’s degree earn salaries that are 53 percent more than young adult
high school completers, and 96 percent more than those who did not earn a high school
diploma. (2010).
Many students in the U. S. leave high school prior to graduation. As of 2000,
African Americans dropped out at a rate of 13.1% compared to their White peers’ rate of
6.9%. When students leave school, it impacts the economic, social, and intellectual
aspects of the community and of society as a whole.
Loveless (2009) used NAEP data to investigate ethnic differences among high
achievers. These students scored at or above the top 90th percentile for their grades in
14
reading or math. Although African Americans comprise 16% of the general population,
only 2.6% of the high achieving students were African American compared to 81.5%
who were White students. African American and Hispanic students scored in the low
achieving range at twice the proportion of their representation in the population of eighth
graders and one-fifth to one-fourth of the proportion of high achievers that would be
expected given the composition of the group as a whole (Loveless, 2009).
School achievement is a function of cognitive ability as well as other personal and
environmental factors. Thus, predicting school achievement from ability alone ignores
the influence of other important variables (Thorndike, 1963). Such ―over-prediction‖ of
student achievement may result in students being labeled as ―underachieving‖ or failing
to live up to expectations when the result is more accurately described as a failure in
prediction (Thorndike, 1963). For example, students must sustain high levels of
motivation and engagement in order to attain high levels of achievement. Including
measures of the components of intrinsic motivation and the student’s perception of the
educational experience may increase the accuracy of the prediction.
Researchers have shown that although students may have similar scores on ability
tests at one time period, different levels of conative and affective characteristics may
impact later achievement. Ackerman (2003) has argued that ―trait complexes‖ of
cognitive and personality characteristics that may be used to better predict performance
than cognitive ability alone. Individuals with different levels of intrinsic or extrinsic
motivation and different goal orientation may demonstrate different levels of
performance even if their cognitive abilities are similar.
15
Aptitude Theory
More than multiple measures are required for predicting student achievement. The
demands and affordances of the context in which learning occurs must also be
considered. In a classic paper, Cronbach (1957) suggested that educational researchers
simultaneously investigate the effect of both individual characteristics and the treatments.
―Applied psychologists should deal with treatments and persons simultaneously.
Treatments are characterized by many dimensions; so are persons.‖ (Cronbach, 1957, p.
680). Collectively, characteristics of persons that predict success in some environment
are called aptitudes. Cronbach also suggested using characteristics of the situation to
understand how the environment may support or diminish expressions of motivation,
affect, and cognitive ability.
Aptitude theory as a way to understand individual differences received significant
attention from Snow (1989). His conceptualization built upon Cronbach’s (1957) original
understanding of aptitude and treatment interactions. Snow (1992) described aptitudes as
―a readiness‖ (p. 9) to learn. This readiness involved three main factors: 1) motivation to
learn the material, 2) affective characteristics that make learning enjoyable (i.e.
curiosity, openness, agreeableness, emotional stability), and 3) the cognitive ability to
grasp the material. These three factors interacted with the characteristics of the learning
environment to determine ―the degree to which persons are equipped to meet the
affordances of particular performance situations.‖ (Snow, 1996, p. 538). The situation
thus may help determine the readiness of the individual to learn in a particular
environment. Put differently, defining the treatment is part of defining the aptitude (Snow
& Lohman, 1984).
16
Snow (1992) described aptitude as including the ―conative and affective
characteristics of persons, not just cognitive abilities‖ (p.9). The interaction between
these traits of persons and the treatment environment (situation) in which they are trying
to learn together explain achievement. ―Aptitude theory should be thought of as linking
science, aimed at descriptive and explanatory concepts that connect the characteristics
and capabilities of persons to features of treatment environments, real or desired, so as to
reach goals of field achievement‖ (Snow, 1992, p. 16). Understanding the characteristics
that an individual brings to the situation can assist in predicting which interventions will
be the most successful in supporting academic achievement.
Other researchers have extended the concept of aptitude-treatment interaction to
describe how a person’s response tendencies or ―propensities‖ (Corno, et al., 2002, p. 47)
interact with the situation. These propensities may be strengthened or weakened
depending on the situation and the environmental response to the individual’s behavior.
As an individual continues to encounter different situations, the propensities that are used
continue to develop and eventually become more general aptitudes for learning in other
situations.
A key component in this conceptualization is the role of the environment in
providing feedback that strengthens or weakens the natural tendencies. A child with little
environmental variation may lack the opportunity to develop some propensities.
Similarly, children who consistently receive negative feedback in specific situations (e.g.
school) may develop generalizations about themselves or future situations that thwart
learning.
17
The situative aspect of this conceptualization recognizes that persons bring to the
situations many response tendencies that could assist or hinder their learning (Snow,
1996). However, individuals in an unfamiliar situation (e.g. different culture) may not
perceive the nuanced clues of the environment that would allow them to bring to bear the
knowledge, skills, and other characteristics that would help them succeed. Such
individuals may perform poorly when compared to others who bring similar resources to
the situation. Including situativity in an explanation of individual differences may add to
understanding performance differences among individuals with similar aptitude
complexes.
Cognitive Ability
The relationship between cognitive ability and academic achievement is well
established. Although the term ―cognitive ability‖ is sometimes used as a synonym for
intelligence, it may also be viewed as designating a broader collection of abilities
(Jensen, 1998; Cattell, 1963). Indeed, all models of human abilities show that intelligence
is a multidimensional construct.
Spearman was the first researcher to empirically analyze intelligence test scores
(Fancher, 1985). His creation of the statistical technique of factor analysis provided a
new way to investigate the relationships between scores on different tests. Spearman’s g
is still used as the overarching explanation for differences in ability. Jensen (1998)
describes g as the ―distillate of the common source of individual differences in all mental
tests, completely stripped of their distinctive features of information content, skill,
strategy, and the like.‖ (p. 74). Jensen (1998) further claims that this common factor is
invariant across different methods of factor analysis and has cross-cultural support.
18
Cattell (1943) decomposed Spearman’s general factor into two broad abilities:
general fluid ability (Gf) and general crystallized ability (Gc). Gc represented an
investment of fluid intelligence in domain specific knowledge. Individual differences in
interests help determine which areas of domain knowledge are developed over time
(Cattell, 1963). Horn (1980) and other researchers found that although Gf tends to
decrease after young adulthood Gc tends to increase.
Carroll’s (1993) hierarchical model of intelligence is the most widely accepted
and widely validated model in use today. This model proposed a stratified hierarchy with
the most psychologically transparent or narrow abilities at the bottom of the hierarchy.
The second stratum is comprised of nine broad cognitive abilities and the final stratum is
comprised of g or a general factor that is shared by the other abilities. The general factor
is the most difficult to define because there are no specific abilities that can be observed
as evidence for g alone. Further, the predictive power of the general factor is generally
less than the predictive power of broad abilities that better match the performance
demands of particular domains.
Brunswikian symmetry provides one way to understand the lack of predictive
power of the most general abilities (Ackerman &Lohman, 2006). This symmetry refers to
matching the complexity or specificity of the ability that is being tested to the task that
the individual is being asked to perform. Accuracy of prediction may be reduced if a
broad ability, as measured by an ability test such as verbal ability, is used to predict
performance on a narrow specific task such as reading fluency. Thus, using cognitive
ability measures to predict performance is complicated both by the measurement
instruments used and the performance task that is predicted.
19
Measuring Cognitive Abilities
Measuring cognitive ability is a complex task. Alfred Binet has been credited with
developing the first intelligence test. The tasks that made up Binet’s test were designed to
be used clinically to determine the mental functioning capabilities of children who were
performing below expectations in school. The most common measure of cognitive ability
has been to the intelligence test. The most common IQ tests given to school-aged
individuals are the Standford- Binet (SB) 5 and the Wechsler Intelligence Scale for
Children (WISC) IV.
Until the 1960s, most IQ standardization populations were White, middle-class
and primarily male (Flynn & Weiss, 2007). During the feminist and civil rights
movements it became apparent to society that White, middle-class children, and adults
were often provided opportunities and educational experiences that other individuals
were denied. These privileges may have resulted in higher scores on IQ tests therefore
standardization samples need to include people who reflect the national population. The
samples were changed with the WISC-R and SB-4 (Dickens & Flynn, 2006). However,
simply including these individuals in the standardization does not somehow correct for
differences in opportunity to develop the abilities measured by the test.
Although difference between the performance of White and African American
students on intelligence tests have declined; they are still substantial (Barton & Coley,
2010). Other environmental variables may contribute to lower scores of ethnic minority
students on standardized IQ tests. Stereotype threat has been found to create anxiety in
African American students which may lower test scores. Language may be an issue for
English Language Learners or for students who have not learned to speak standard
20
academic English. Nonverbal ability tests have long been used in an effort to reduce the
impact of culture on measures of cognitive ability. However, such measures at best
reduce the impact of language and culture (Anastasi, 1980). Sometimes nonverbal tests
are even more culturally loaded than tests that rely on language (Scarr, 1984).
Genetics and Intelligence
Throughout history, researchers have been interested in determining how people
think and why some seem to be capable of advanced learning. Galton described
intelligence as a highly heritable trait that was innate and fixed. Others such as John
Stuart Mill have described intelligence and the creation of knowledge as highly
influenced by nurture. In an attempt to disentangle the effects of the environment from
genetics, studies, such as the Minnesota Twins Study (Bouchard, Lykken, McGue, Segal
& Tellegen, 1990), have allowed researchers to estimate the heritability of cognitive
abilities. Bouchard et al., (1990) estimated that 50% of the variation in IQ scores could be
attributed to genetic factors. Of course, this finding may also be interpreted as 50% of the
variation in IQ scores was attributed to the environment. When Turkheimer, Haley,
Waldorn, D’Onofrio and Gottesman (2003) studied twins of various levels of SES, they
found that environment played a larger role in explaining IQ variance for low SES
individuals and the exact opposite was true for high SES individuals. Specifically,
heritability (h2) estimates ranged from .10 for low SES children to .72 for high SES
children. ―All abilities – physical and cognitive- are developed through exercise and
experience. There are no exceptions.‖ (Lohman, 2005b, p. 119). Thus, while nature may
provide the potential, nurture provides the opportunities for exercise and experience that
allow for the development of potential.
21
The common tendency to view ability and achievement as qualitatively distinct
concepts can in part be attributed to the use of different terms to label the constructs.
Kelley (1927) called this the jangle fallacy. Because the concepts have different names
we assume that they must be different. The jangle fallacy gives the false impression that
these are two distinct constructs and that measurement tools can be created that measure
each unambiguously. Cognitive abilities are often conceptualized as the raw materials
which students bring to academic institutions. Achievement tests are seen as the
measurement by which evaluations can be made as to the extent of using given cognitive
abilities, students were able to understand and learn grade level information. Lohman
(2005b) provides an appropriate analogy for the fallacy of trying to create such a
distinction.
Arguing that a good measure of reasoning ability should be
independent of motivation, experience, education, or culture is like
saying that a good measure of physical fitness should be
independent of every sport or physical activity in which the person
has engaged. (p. 119)
Corno, et al. (2002), provide another view of cognitive abilities and achievement
test scores. In their view, achievement test performance, or performance at any time is a
function of the interaction of past experiences, motivation, and cognitive ability or in a
word, readiness. The readiness of an individual is an evaluation of the extent that past
experiences and cognitive abilities have impacted an individual’s capacity to benefit from
instruction in an area at a particular time. Any measure of performance is a function of
these three components and therefore it is not possible to separate cognitive ability from
the other components or from past experiences of opportunities to learn and past
motivation to learn.
22
Many theorists describe an ability-achievement continuum by which measurement
instruments may be described as measuring more crystallized or more fluid intelligence
(Lohman, 1993; Sternberg, 1985; Cronbach & Furby, 1970). This continuum places
ability tests which measure general problem solving or non-content specific tests at one
end. Achievement tests which are measures of specific content taught at various grade
levels of school are at the opposite end of the continuum. Thus, the exposure to content is
the determining factor of where on the continuum a measurement would lie. Measures of
academic achievement therefore not only reflect the cognitive ability of the students but
their ability to make use of the instruction provided to master components of the
curriculum.
Cognitive ability has been linked to almost every measurable outcome. The most
common in educational research are academic achievement, school behavior, and family
characteristics. As would be expected, strong positive relationships have been found
between cognitive ability and academic achievement. Research on cognitive ability and
academic achievement has shown that g or IQ can account for approximately 50% of the
variance associated with academic achievement as reflected in GPA (Jensen, 1998).
While this is a significant percentage of variance, it leaves another 50% that may be
impacted by other factors. Therefore, it is important to examine other possible
contributors to academic achievement to understand how to help students maximize their
performance.
Racial Identity
Recent research has examined the role of racial identity in school achievement.
Identity formation is thought to develop with age and become stable in adulthood. Marcia
23
developed a framework for categorizing identity formation based on the amount of
exploration and commitment an individual demonstrates when investigating different
identities (1994). Identity exploration typically begins in early adolescence and
individuals reach a stable identity in adulthood although some life experiences (e.g.
getting married, having a child) may change an individual’s identity perception. The
result of participating in various roles leads individuals to create hierarchies of identities
(Stryker & Serpe, 1994). An individual may have an identity as a man, an African
American, an executive, and a father, and may behave differently based on the identity
that is salient in a given situation. For example, Barack Obama has the identity of the
President of the United States, the leader of the Democratic Party, an African American
man, a husband, and a father to his children. Any of these roles will be salient in a given
situation and his behavior will correspond to his perception of the identity that is most
important at the time. Similarly, an African American student may exhibit codeswitching, behaving differently in home and school settings, if these environments
require different ways of speaking and use different vocabulary.
Racial identity differs from most personal identities because of its invariance and
the public nature of its expression. An individual would have to take deliberate steps to
conceal or change his or her racial identity. ―Unlike a personal identity, such as
occupation, ethnicity cannot be chosen by the individual, but rather it is determined at
birth or assigned to one by others on the basis of ethnic background or phenotype.‖
(Phinney & Ong, 2007, p. 275). However, most models of racial and ethnic identity
recognize that even though society may assign an individual to a particular group, his or
her response to acceptance of that assignment may vary with age and experience
24
(Rowley, Sellers, Chavous, Shelton, & Smith, 1998; Cross, 1991; Phinney, 1989).
Models of racial and ethnic identity formation may be similar to identity theory in
identifying stages through which an individual is expected to progress (Cross, 1991) and
that racial and ethnic identity is stable once established (Phinney, 1989; Sellers et al.,
1997, Yip, Seaton, Sellers, 2006).
Although there is overlap between racial and ethnic identity, they are separate
constructs (Phinney, 1989; Sellers, et al., 1997). Ethnic identity may be defined as ―a
sense of self, but it differs [from personal identity] in that it involves a shared sense of
identity with others who belong to the same ethnic group.‖ (Phinney & Ong, 2007, p.
274). Membership in the group is based on common culture, language and history
(Cokley, 2007). For example, Irish and Italian are ethnic identities. Race is based on
phenotypic characteristics. In the previous example, the race would be White. Black
racial identity is described by Sellers, et al., (1998) as ―the significance and qualitative
meaning that individuals attribute to their membership within the Black racial group
within their self-concepts.‖(p. 23). Thus while both address group membership, ethnic
identity is more focused on sense of an individual’s identification with a particular group
(a measure of degree). Racial identity is more focused on the importance and meaning
that group membership has for the individual (a qualitative measure). Cokley (2007)
advises the choice of the appropriate lens by which to view ethnic and racial identities
depends on the focus of the research.
When researchers are interested in how individuals see themselves
relative to their cultural beliefs, values, and behaviors, ethnic
identity is the appropriate construct to study. However, when
researchers are more interested in how individuals construct their
identities in response to an oppressive and highly racialized
society, racial identity is the more appropriate construct to study.
( p. 225)
25
Because racial and ethnic identities are different constructs, different
measurement tools have been created to operationalize these variables. Ethnic identity
refers to a sense of belongingness to a group or culture (Phinney & Ong, 2007).
Consequently, measures of ethnic identity also have measures of racelessness or other
group orientation to describe individuals for whom belonging to the ethnic group is not a
significant factor in their identity. Racial identity reflects the role that society imposes on
an individual as a member of a group (Sellers, et al., 1997). Society’s response to an
individual of color sends messages regarding the acceptance and value that is placed on
individuals who are not a part of the majority culture. Racial identity instruments seek to
measure the degree to which individuals accept the labels that society places on them, the
degree to which positive and negative societal messages are accepted, and how meaning
is made by individuals.
Sellers, Smith, Shelton, Rowley, and Chavous (1998), developed the
Multidimensional Model of Racial Identity (MMRI) and subsequently the
Multidimensional Inventory of Black Identity (MIBI) to measure specific components of
racial identity in African Americans. Unlike the Cross Racial Identity Scale (CRIS)
(Vandiver, Cross, Worrell, & Fhagen-Smith, 2002) which is stage based and whose
scores reflect judgments about an individual’s identity development, the MIBI regards
racial identity as both situational and stable. ―Little longitudinal research has been
produced that demonstrates the validity of the concept of individuals’ cycling through the
four stages proposed by the model.‖ (Sellers, et al., 1998, p. 22). The Multidimensional
Model of Racial Identity (MMRI) focuses on the status or strength of the areas of identity
26
rather than the progression of the development of racial identities. There is no final racial
identity to be achieved.
There are some components in racial identity that are measured by the MIBI, such
as regard and centrality, which are more stable within an individual. Other components
such as salience reflect the situativity of race being more or less salient in a given
situation (Sellers, et al., 1998). Thus, because situations change, salience cannot be
measured by survey instruments. The MIBI allows measurements based on subtest scores
regard, centrality, and ideology.
Regard is the individual’s perception of public opinion of African Americans in
general as well as the individual’s personal perception of African Americans. These
perceptions are developed through life experiences and environmental interactions.
Judgments of public opinion of African Americans may be influenced through societal
messages received through the media, in school or when interacting with other members
of the community. Personal regard for African Americans reflects the individual’s view
towards other African Americans and membership in the group. The extent of positive or
negative feelings and affect comprise the component of regard.
Centrality refers to the importance of racial identity to the individual. Sellers, et
al., (1998) indicated the hierarchical structure of identity. Centrality describes the extent
that racial identity is a core component in the individual’s identity. It reflects the
importance of race in how individuals define themselves.
Ideology describes individual perceptions of how African Americans should
behave in political, economic, and social interaction with other racial groups. This
component may be subdivided based on the extent of the philosophy of interaction on the
27
continuum of nationalist to humanist where behavior is characterized by more or less
assimilation and interaction with the dominant group. Certain ideologies are not judged to
be better than others in the MMRI; rather ideology is recognized as a component in the
racial identity that may help to explain behavior.
Using the three dimensions, clusters of scores have been used to develop profiles
of individuals. These clusters may be formed on the basis of scoring high or low on
centrality and regard and the preferred ideology. The profiles give researchers the ability
to use a more fine grained approach in understanding the specific components of racial
identity that are interacting with the independent variables. This refined approach may
help explain academic achievement by developing understanding of the roles of racial
identity factors for high and low achievers when cognitive ability and other factors are
held constant. ―What is not yet known is whether different identity profiles have
differential relationships with self-efficacy and intrinsic motivation, with concomitantly
different implications for achievement.‖ (Worrell, 2009, p. 145). Thus, research is needed
that will help explain how different racial identity factors may interact with other
important variables to impact academic achievement.
Chavous, Bernat, Schmelk-Cone, Caldwell, Kohn-Wood, & Zimmerman (2003)
found centrality, private, and public regard clustered to create four profiles; Buffering –
1) high centrality, high private regard, low public regard, 2) Low Connectedness – low
centrality, high private regard, low public regard, 3) Idealized – high centrality, private
regard, and public regard, 4) Alienated – low centrality, extremely low private regard and
low public regard. These four clusters were found to have significant group differences
in educational attainment. The individual components of racial identity may provide
28
insight to the differences in performance. Chavous et al. (2003) found that public and
private regard were significantly correlated to school attachment and school relevance for
12th grade African American students. Private regard was also significantly correlated
with school efficacy and school importance. School relevance and school efficacy were
also significantly positively correlated with racial centrality.
In a similar study, Harper and Tuckman (2006) used the MIBI to create cluster
profiles for 9th and 12th grade students. The four profiles demonstrated significant
differences in academic achievement with students possessing the alienated profile
displaying higher grade-point averages for both 9th and 12 grades. The situativity of the
racial identity may be reflected in this study by low centrality of race because the
students may have a pro-achievement academic orientation. In the hierarchical ordering
of identities, the students appeared to give more salience to academic achievement than to
race (Harper & Tuckman, 2006).
Worrell has examined ethnic identity and other group orientation as it relates to
academic achievement for academically gifted African American students using the
MEIM developed by Phinney (1989). His research on ethnic identity and its relationship
to academic achievement is important; one finding of the investigations of stereotype
threat was that individuals who have high confidence in the domain may be particularly
vulnerable to situational cues that indicate a threat may be present. In his study of gifted
students, Worrell (2007) found that only African American students’ scores on other
group orientation predicted self-esteem. Ethnic identity was not a significant predictor,
although it was negatively correlated with achievement for gifted African American
students. Similar to the students in the Harper and Tuckman (2006) study, Worrell
29
proposed that perhaps the need for academic achievement was stronger in these high
ability students than was the need to identify with their ethnic group.
The situativity of the educational process was re-emphasized by Worrell (2009),
―Academic achievement is not merely an individual endeavor; rather, it occurs in a social
context and is framed by one’s perceived position in the social structure of the society‖(p.
138). While many researchers purport that the best indicant of achievement in the
immediate future is achievement in the immediate past (Lohman, 2005a), for students
from minority groups, the social structure of previous educational conditions may have
erected barriers that impeded the students’ ability to ―experience and exercise‖ their
abilities (Lohman, 2005b, p. 119). Educators play an important role in helping to remove
the structural barriers, such as stereotype threat and teacher expectations, and provide
opportunities for students to develop academic aptitudes.
When people are members of a group about which a negative stereotype is known
to exist, and they encounter a situation where there is a risk of confirming the negative
stereotype, a stereotype threat occurs (Steele & Aronson, 1995). The threat is perceived
as a self-evaluation, a threat that may affect performance. Individuals may experience
increased anxiety when they encounter difficulties in the task as a result of the known
stereotype. When performance diminishes because of the stress on working memory due
to increased anxiety, individuals may lower expectations about performance. Over time,
these lowered expectations may translate into decreased motivation or diminished value
of the activity.
The cumulative effect of schooling and prolonged exposure to stereotype threat
may manifest itself in increased feelings of anxiety and decreased motivation (Seaton,
30
Yip, & Sellers, 2009). In an effort to protect their self-concepts, individuals may develop
disidentification (Steele, 1997). Disidentification removes the domain area from
importance in self-concept. The individual no longer cares about the domain.
Performance is no longer a reflection of the person’s capabilities. Disidentification can
negatively impact a person’s identification with school and over time result in
underachievement for those in the stereotyped group.
Steele and others have indicated that stereotype threat exists in situations beyond
standardized tests and may cause non-White students to feel less able to participate in an
academic setting (Purdie-Vaughns, Steele, Davies, & Ditlmann, 2008; Steele, 1997;
Steele & Aronoson, 1995). ―Different groups experience different forms and degrees of
stereotype threat because the stereotypes about them differ in content, in scope, and in
situations to which they apply‖(Steele, 1997, p. 618). The situational and individual
differences that are experienced make treatments to treat stereotype threat difficult to
design and test. Stereotype threat echoes Snow’s (1989) aptitude-treatment interaction
conceptualization that identified individual aptitudes interact with situational demands
and opportunities with different manifestations.
The ability to recognize the constraints and affordances of a situation is a defining
factor in the development of aptitudes (Snow, 1996). Stereotype threat may be seen as an
aptitude for recognizing threatening aspects of situations that are salient to those of
minority populations. Purdie-Vaughns, et al., (2008) showed how settings provided cues
that signaled the degree of threat or safety an African American could expect to
experience. Understanding the cues that a school setting may be sending to minority
students may help to explain academic achievement. ―When cues convey the threat of
31
identity-contingent evaluations, trust in the setting can be undermined. When cues signal
affirming contingencies or evaluations that are not identity-contingent, trust can be
sustained‖ (Purdie-Vaughns, et al., 2008, p. 626). Students who have high confidence in
the domain may experience even greater stereotype threat (Steele, 1997). Thus those
students who have high cognitive ability and high motivation may be even more
susceptible to stereotype threat. Their high cognitive ability may make them more aware
of broadly held negative views of others. Their high motivation may make them more
aware of barriers, such as the negative opinions of others, which may be preventing them
from reaching their goals.
Fordham and Ogbu (1986) purport that minority groups develop a cultural frame
of reference that defines situational activities, behaviors, events and symbols as
representing the dominant (White) culture. Academic achievement may be viewed as a
White activity and thus would be seen as something minorities would not expect to attain.
Individuals who attempt to participate in White culture (i.e. achieve in school) or ―act
White‖ may receive negative feedback from their peers or community. Fordham and
Ogbu (1986) offer this cultural framework as one possible explanation of seemingly able
African American students performing below their ability levels.
While ―acting White‖ has received much attention in the literature, there have
been mixed results in empirical replication of the finding. Tyson and Darity (2005)
argued that adolescents from all races display oppositional attitudes as they separate from
parents in Western cultures. Peers become important and academic mediocrity is
commonplace. These researchers believe that certain school cultures may increase
negative interactions between high and low achievers or within and between racial and
32
socioeconomic groups. Witherspoon, Spreight, and Thomas (1997), reported that African
American high school students who had immersion attitudes – involving oneself in Black
culture and withdrawing from interacting with persons form the dominant culture,
displayed a statistically significant negative correlation between this attitude and gradepoint average. Thus, the implication of the students’ perception of the school climate
toward academic achievement and racially diverse students would benefit from further
review.
Measuring Racial Identity
Sellers, et al., (1997) performed initial analysis of the Multidimensional Inventory
of Black Identity (MIBI) using 474 African American college students from two
medium-sized universities. One university was predominantly Black (185 participants)
and the other was predominantly White (289). Confirmatory factor analysis was
conducted using the Kaiser-Meyer-Olkin test to evaluate the appropriateness of a three
factor solution. The results indicated that the MIBI measured three interrelated
constructs. Factor analysis was performed using maximum likelihood extraction of three
factors with a Promax rotation. Questions which loaded less than .30 on any factor were
eliminated from the instrument. Factor analyses were then conducted for the items
loading on each subscale to determine that the questions were measuring only one
construct. The one-factor structure was supported for Centrality however two items were
dropped due to factor loadings below .30. A two-factor structure was investigated for
Regard, however, three of the public regard questions had to be dropped due to low factor
loadings thus public regard was eliminated from the scale it Sellers et al noted that further
development of this scale was necessary. All of the private regard questions loaded at
33
appropriate levels thus the Regard scale consisted of these seven items. The ideology
scales were further analyzed using a four factor matrix and questions were eliminated that
failed to load above .30. The result was the 51 item MIBI.
Cronbach’s alpha for the scales were as follows: Centrality (.77), Regard – Private
(.60), Assimilation (.73), Humanist (.70), Minority (.76) and Nationalist (.79).
Correlations between the scales were examined to test the hypothesized relationships. An
example of a hypothesized relationship was that ―individuals for whom race was central
were also likely to have positive private regard for African Americans‖ (Sellers, et al,
1997, p. 810). This relationship had a correlation of r=.37 which was significant. Other
expected relationships involved the different ideologies and their relationships to racial
centrality and private regard. All of the predicted relationships were supported by
correlations, most of which were significant. Criterion validity was assessed by
measuring relationships predicted by the MIBI and having an African American best
friend or taking Black studies courses. As expected, those students who had high racial
centrality and high private regard reported having frequent contact with other African
Americans.
Walsh, (2001)
Walsh (2001) conducted a validation study of the 51 item MIBI with 95 Black
Britons living in London. These participants ranged in age from 18-60 with the majority
of participants aged 18-30. The correlations between the subscales were very similar to
those found by Sellers, et al., 1997. Centrality and Private Regard had a correlation of r
=.40 in this sample whereas Sellers et al. (1997) all found r = .37. All other correlations
were similarly close in value. For both groups, Private Regard had the highest mean
34
rating which could be interpreted as both groups had strong positive feelings about being
a member of the Black community. A difference between the American and Briton
groups was found on the ratings of Centrality. The American group rated this subscale as
the second highest mean score whereas the Britons rated it as second lowest.
In an attempt to replicate the predictive validity study by Sellers et al.,1997,
Walsh used contact with Black people (amount of time spent with Black and White
people) as the dependent variable. Contact with Black people followed the American
pattern of positive, significant correlations with Centrality, Private Regard, and
Nationalist Ideology. Contact with White people was somewhat different between the
groups in that the Britons’ correlations were smaller and not significant although the
directionality remained the same. Based on this study, Walsh concluded that the MIBI
was a valid measure of racial identity for Black Britons (2001).
Cokley and Helm, (2001)
Cokley and Helm (2001) assessed the validity of the MIBI with additional items
for the Public Regard scale using confirmatory factor analysis and item analysis. Cokley
and Helm also used the African Self-Consciousness Scale to determine the concurrent
validity of the MIBI. A total of 279 African American college students from three
predominantly White universities and four historically Black universities comprised the
sample group. The age range of this group was 18-46. Cronbach’s alpha for the subscales
in this study were as follows: Centrality (.73), Private Regard (.76), Public Regard (.74),
Assimilationist (.75), Humanist (.72), Oppressed Minority (.83) and Nationalist (.78).
These alpha coefficients were similar to those found by Sellers et al. (1997).
Confirmatory factor analysis was performed in which seven factors were extracted from
35
the 56 variables. The model allowed items to correlate based on previous studies. Eleven
items stood out as misfitting and the model statistics were less than predicted Chi-square
(1465, N=279) = 2921.200, p<.001; NFI = .56; CFI = .71; SMR = .09).
Concurrent validity of the MIBI was supported by comparing scores on subscales
such as Centrality and Private Regard to the African Self-Consciousness Scale. The
hypothesized relationships were supported. For example, individuals for whom race
centrality and private regard was high also reported high scores in African selfconsciousness. Similar relationships were found in the ideology subscales and
correlations with the African Self-Consciousness Scale.
Confirmatory factor analysis was performed in a similar manner as Sellers, et al.
including the Public Regard subscale items. In this analysis, all but one of the items for
the Public Regard subscale had factor loadings >=.30. The item that did not load above
.30 was ―Blacks are not respected by the broader society.‖ Thus Cokley and Helms
(2001) provide additional support for including the Public Regard subscale in the
instrument. The item analysis focused on the Ideology subscales and noted several
problems with the different philosophies not being clearly defined. The authors did not
mention any areas of inconsistency or difficulties for the subscales of Centrality, Public
or Private Regard.
Intrinsic Motivation
Several researchers have developed theories to explain motivation, especially in
academic settings. Expectancy-value theory (Berndt & Miller, 1990) states that the
expectation of a positive outcome and the value that is placed on the outcome influences
the amount of effort that is exerted towards the goal. Meece and Holt (1993) and other
36
researchers suggest that individuals who adopt performance goals or mastery goals may
display different motivational effects such as persistence and risk-taking (Blackwell, et
al., 2007; Spray, et al., 2006; Cury, et al., 2006). Beliefs about the nature of academic
ability also impact the effort put forth and types of goals that the individual pursues
(Dweck, 1999). The social cognitive theory of motivation argues that an individual’s selfefficacy plays a large role in the energy an individual is willing to invest in pursuit of
attaining a goal (Bandura, 1982). Self Determination Theory regards components of
intrinsic motivation as human needs and the degree to which these needs are met will
determine the level of motivation that an individual displays (Ryan & Deci, 2004).
Beliefs that academic ability is either fixed or malleable significantly affect
motivation, persistence and ultimately academic achievement (Frunham, ChamorroPermuzic & McDougall, 2002; Dweck, 1999). Individuals who hold entity belief view
intelligence as fixed, a natural endowment that cannot be changed. Those who hold
incremental beliefs view intelligence as malleable and able to be improved with practice
and experience. Studies have shown that students’ beliefs regarding abilities directly
impact performance (Blackwell et al., 2007). Students who hold incremental beliefs are
more likely to engage in practice such as homework that will improve skills and
performance. Students who hold entity beliefs are more likely to have more performance
goals and avoid tasks in which they may fail (Blackwell et al., 2007; Meece & Holt,
1993). Incremental and entity beliefs may affect students’ ability to set goals and to
persist in efforts to achieve those goals even when the environment is not supportive.
An important component of intrinsic motivation is perceived competence, or selfconcept. Self-concept is regarded as a multidimensional construct with an overarching
37
general component that is useful for predicting general behaviors and specific
components such as academic, physical and social that is useful for examining and
predicting specific behaviors such as persistence in homework and training, and the
ability to interact with others. Academic self-concept has been found to have a significant
impact on achievement (Marsh, Trautwein, Ludtke, Koller, 2008; Davis-Kean,
Husemann, Jager, Collins, Bates, Lansford, 2008; Cokley & Chapman, 2008; Awad,
2007). However, due to limitations on sample size and the length of the survey
instrument, this area will not be investigated in this study.
Self Determination Theory, a model of motivation developed by Ryan and Deci
(2004) argues that intrinsic motivation is comprised of three components; perceived
competence, perceived autonomy and perceived connectedness. Intrinsic motivation is a
powerful factor that influences persistence and achievement. Ryan and Deci (2004)
describe intrinsic motivation as self-directed action to achieve a goal for the pleasure that
is derived from participating in the task. Individuals who are intrinsically motivated for
academic learning seek out new opportunities to explore unfamiliar subjects for the
inherent enjoyment in learning. High ability students may experience decreased intrinsic
motivation because they do not feel intellectually connected with their peer group or feel
constrained by the pace or level of material being taught in the classroom.
Research on the relationship between academic achievement and the intrinsic
motivation of African American students has shown mixed results. Connell, Spencer, and
Aber (1994) used three samples of African American students (n = 114 - 399) to
investigate relationships between components of intrinsic motivation for racial minority
groups and components of intrinsic motivation. Although the significance levels and
38
incremental R2 varied by sample, perceived competence and perceived relatedness
correlated significantly with behavioral and emotional engagement. These engagement
measures significantly predicted attendance, test scores, and grades. Perceptions of
parental support, competence, and relatedness were found to have stronger relationships
with students’ actions than neighborhood socioeconomic status or gender.
Corpus, McClintic-Gilbert, Hayenga (2009) found a positive, symmetric
relationship over time between intrinsic motivation and academic achievement for third
through eighth graders. Fall intrinsic motivation significantly and positively predicted
spring GPA (β = .06) and that fall GPA significantly and positively predicted spring
intrinsic motivation (β = .06). The authors suggested one interpretation of these findings.
They argued that positive internal drive of intrinsic motivation for academic learning may
assist students in high persistence and performance on academic tasks. Similarly, the
positive effects of high GPA or academic honors may increase perceived competence
which would increase intrinsic motivation. This synergistic relationship may promote a
positive cycle of achievement and increased self-concept for learners who experience
positive educational environments.
Conflicting results were found by Gagné and St. Père (2002). Their study of high
school females found that once IQ was controlled, intrinsic motivation did not
significantly improve the prediction of achievement. In this study IQ and intrinsic
motivation were not correlated (r = .03). Spinath, Spinath, Harlarr, & Plomin (2006)
found that although intrinsic values, how much children indicated they enjoyed the
activities of the subject, were positively correlated with math and English (.20 and .26),
they did not significantly add to predicting academic achievement after controlling for
39
IQ. The correlation of g and intrinsic values in their study of 9 year olds was .08-.13.
Intrinsic values were correlated with math and English at .20-.26 respectively. However,
intrinsic values (interest) did not add significantly to the prediction of achievement.
Cury, Elliot, DaFonseca and Mooler (2006) used a social cognitive model to
explore the relationships between implicit theories of ability and achievement goals, and
the role of perceived competence as a moderator of these relationships in two studies. In
the first study, entity theory was a negative predictor of math performance (β = -.14) for
465 French students with an age range of 12-14 years. Incremental theory of ability was a
positive predictor (β = .19) of mastery goals. Perceived competence significantly
predicted mastery-approach and performance approach goals. However, perceived
competence did not moderate the relationships between the students’ personal theories
regarding abilities and achievement goals. The second study examined the relationship
between intrinsic motivation and achievement goals using regression analysis. In this
study, mastery-goals were significant positive predictors. Furthermore, the relationship
between beliefs regarding ability and intrinsic motivation were mediated by achievement
goals, namely mastery-approach and performance-approach goals, which reduced the
strength of the relationship.
The Cury et al. (2006) study supports Dweck’s theory (1999) regarding beliefs
and performance. However, this study also underscores the complexity of the relationship
between motivation and performance. The mediating role of mastery-approach goals on
the relationship between ability beliefs and intrinsic motivation may illustrate the need to
understand the roles that goals may play in explaining performance. In this study,
perceived competence was a significant predictor of achievement goals. However,
40
perceived competence did not moderate test performance or intrinsic motivation. This
finding does not support the assumption in Self-Determination Theory that perceived
competence is a key element in intrinsic motivation.
Other research has explored differences in motivation among minority groups.
Areepattamannil and Freeman (2008), explored motivation differences in 573 immigrant
(n=266) and non-immigrant (n=307) grade 11 and 12 Canadian students. This research
found significant group differences on measures of extrinsic motivation-external
regulation, extrinsic motivation-introjected regulation, intrinsic motivation to know,
intrinsic motivation to accomplish, and intrinsic motivation to experience stimulation.
The strongest variables for differentiating between the groups were the intrinsic
motivation measures with immigrants scoring higher than non-immigrants. Despite
significant group differences, none of the motivation variables significantly predicted
English or Math grade point average. This study is important when trying to understand
motivation because it highlights differences in motivation that may exist between
members of the dominant culture (non-immigrants) and members of a minority culture
(immigrants).
Unrau and Schlackman (2006) found a small but statistically significant
relationship between ethnicity and intrinsic motivation for 1,032 students (n=768
Hispanic, n=264 Asian). In this study, ethnicity also significantly predicted reading
achievement. The interaction of the ethnicity and intrinsic motivation on reading
achievement was also statistically significant. In this study, intrinsic motivation for
Hispanic students had a lower standardized path coefficient (.19) than for Asian students
(.30). These findings highlight how intrinsic motivation may function differently for
41
different groups. The levels of intrinsic motivation were significantly different (d = -.15)
by group as were the strengths of the relationship between intrinsic motivation and
reading achievement.
Some researchers have found an unusual relationship between some components
of motivation and academic achievement for African American students. For the past
decade, researchers have been investigating the attitude-engagement paradox
(Mickelson, 1990). Specifically, African American students reporting higher levels of
academic engagement and more positive attitudes towards education than European
American students still perform at a lower level in school. In his review of literature on
the motivation of African American students, Cokley (2003) found results that support
this finding.
Long, Monoi, Harper, Knoblauch, and Murphy (2007) found that for eighth and
ninth grade African American students, academic self-efficacy was high while
achievement was low. This study found that some students chose to underachieve due to
their perceptions of a negative school climate and lack of school support. However, the
same students were able to maintain a strong belief in their own capabilities.
African American students who adopt oppositional identities to
combat the negative impact of actual and perceived discrimination
within their school setting may feel confident in their ability to
successfully execute a given academic task or be interested in an
academic domain and still simultaneously express a deliberate
disdain for academic behaviors associated with successful
outcomes. (Long et al., 2007, p. 214).
Shernoff and Schmidt (2008), used data from the Sloan Study of Youth and Social
Development to investigate group differences in academic engagement and attitudes
among U.S. high school students (N=586). This study reported African American
students’ grade point averages as .18 grade lower than European American students (ES
42
= .72). Engagement of African American students was higher than that of European
American students (effect size = .51). African American students in this study also
reported higher levels of intrinsic motivation than European American students (effect
size = .43). An interesting finding of this study is the negative interaction of the term
black * engagement for predicting grades. Thus in this case, engagement may be
operating differently with grades based on the race of the participants. Relationships that
appear to make intuitive sense, such as higher engagement being strongly correlated with
higher achievement, may not be universal and may even go in the opposite direction for
some students. This requires researchers to explore the role of specific components of
motivation for African American students.
Measuring Academic Motivation
The Academic Motivation Scale was created to measure academic motivation in
high school students. The measure was originally created in French. As a means of
establishing reliability and validity of the English instrument, 745 university students
from Ontario completed the survey (Vallerand, Pelletier, Blais, Briere, Senecal, &
Vallieres, 1992). Racial composition of the participants is not given in the study.
Cronbach’s alpha from this study were as follows: Amotivation (.85), External
Regulation (.83), Introjected Regulation (.84), Identified Regulation (.62), Intrinsic
Motivation to Know (.84), Intrinsic Motivation to Accomplish (.85), Intrinsic Motivation
to Experience Stimulation (.86). Confirmatory factor analysis was utilized to support the
structure of the model. A seven factor model that allowed correlations between pairs of
26 measured variable residuals showed that the model fits the data as expected, NFI =
.93,GFI = .94, X2 = 748.64, df = 303, p<= .001. Additional analysis showed that the
43
addition of the residuals to the model did not bias the interpretation. This factor analysis
was similar to the original French-Canadian version. The three intrinsic motivation
subscales of Intrinsic Motivation to Know, Intrinsic Motivation to Accomplish and
Intrinsic Motivation to Experience Stimulation correlated r = .58 - .62 with each other.
The extrinsic subscales of External Regulation, Introjected Regulation, and Identified
Regulation correlated r = .29 - .48 with each other. When ANOVA analysis was
performed on the basis of participant’s sex, a significant sex * instrument interaction was
noted F(6,738) = 3.87. Females scored higher on all three intrinsic motivation subscales
than male participants (Vallerand, et al., 1992).
Cokley, (2000)
Cokley (2000), sought to examine the validity of the AMS by replicating the
study within a diverse population of American students. The participants were 263
undergraduates (39 African Americans, 181 Euro-Americans, 6 European Internationals,
2 Asian Americans, 8 Asian Internationals, 6 Hispanic Americans, 16 individuals who
described their ethnicity as ―other‖, and five participants who did not disclose their
ethnicity). This study showed significant, positive correlations within the two main areas
of the scale – intrinsic and extrinsic motivation. The intrinsic motivation subscales had
correlations of r =..58 - .68. The three extrinsic subscales had correlations ranging from r
= .45-.50. These correlations are similar to those obtained by Vallerand et al. (1992).
Cokley (2000) hypothesized that the continuum of self-determination should be reflected
in scores that ranged from low to high moving from Amotivation to Extrinsic Motivation
Subscales to Intrinsic Motivation Subscales. In this study, there was a stronger
relationship between the Intrinsic Motivation to Accomplish subscale and Identified
44
Regulation and Introjected Regulation than the three extrinsic motivation subscales had
with one another. While this result could indicate the possibility of overlap within the
subscales, the study did find a clear distinction between extrinsic and intrinsic motivation.
Cokley, Bernard, Cunningham and Motoike, (2001)
In an effort to provide further psychometric evidence for the validity of using the
AMS with U.S. students, Cokley, Bernard, Cunningham, and Motoike (2001), conducted
factor analysis and concurrent validation studies using data from the 2000 study.
Cronbach’s alphas reported in the 2001 study were as follows: Amotivation (.86),
Extrinsic Motivation External Regulation (.81), Extrinsic Motivation Introjected
Regulation (.86), Extrinsic Motivation Identified Regulation (.70), Intrinsic Motivation to
Know (.83), Intrinsic Motivation to Experience Stimulation (.81), and Intrinsic
Motivation to Accomplish (.85). Thus the internal consistency of the survey was very
similar to the results obtained by administering the AMS to the Canadian participants.
Confirmatory factor analysis was utilized to assess the validity of the internal
structure of the AMS. The seven factor model revealed the best fit with the data (CFI =
.90, NFI =.83) however the chi-square value was statistically significant. These findings
were also similar to those found by Vallerand et al. (1993). Cokley et al. (2001) used the
AMS subscale correlations with academic self-concept to establish construct validity.
Significant, positive correlations were found between the three intrinsic motivation
subscales and academic self-concept (r = .39, .32, and .25). There were no correlations
between the extrinsic motivation subscales and academic self-concept. The amotivation
subscale correlated with academic self-concept r = -.47 which was significant. None of
the AMS subscales correlated significantly with self-reported GPA, which is contrary to
45
the results of Vallerand et al. (1993). Construct validity was further tested by
investigating group differences using t-tests with a Bonferroni correction. The only group
difference on seven subscale scores that was noted was that of African Americans scoring
significantly higher on Extrinsic Motivation External Regulation than other participants.
There were no other gender or ethnic differences found.
School Support
Self-concept, racial identity, intrinsic motivation, and aptitude theory all place
significant importance on the frames of reference, or situations in which an individuals
are interacting. The situation, as perceived by the individuals, influences the behavioral
response. It is important to recognize the affordances and constraints of any given
situation as perceived by the individual. ―Context is critical for both the development of
ability and the expression of it.‖ (Lohman, 2005b, p. 118). Due to the lack of individual
control, academic settings play an influential role in perceived social context for the
individual. Those students who perceive the social context of school as supportive and
nurturing will have a different educational experience than those who perceive it as
limiting or antagonistic. The National Research Council (2004) found that ―students’
motivation develop partly as a consequence of the educational environments they
experience.‖ (p. 33). Thus the social context or school climate that students experience
may influence academic achievement.
Numerous studies have been conducted on the role of perceived social context
and academic achievement (McMahon,Wernsman, & Rose, 2009; Stewart, 2008; Suldo,
Shaffer, & Shaunessy, 2008; Sanders, 1997). Perceived teacher support, perceived school
acceptance and nurturance, and perceived autonomy have been found to play significant
46
roles in motivation and achievement (Cokley & Chapman, 2008; Ames, 1992). Marshall
and Weinstein (1986) found that students as young as first grade were able to identify
teachers who treated students differently based on perceived academic ability.
Differences noted by teacher behaviors, include, positive relationships, positive display,
praise, buffered criticism, positive academic evaluation, and positive behavioral
evaluation. Teachers who emphasized effort and did not treat groups of students
differently were rated more positively by students for teacher-students interactions.
Conversely, on average, teachers who emphasized ability and treated students
differentially were rated low for teacher-student interaction. An interesting aspect of this
study involved the variance in scores by grade-level for similar teacher behaviors. This
difference suggests that the age of the child may indicate the level of awareness the he or
she possesses to recognize differences in the environment.
School Climate
School climate may be considered as the ―quality and consistency of interpersonal
interactions within the school community that influence children’s cognitive, social, and
psychological development.‖ (Haynes, Emmons, & Ben-Avie, 1997, p. 322). Classroom
structures may contribute to the overall school climate. Structures typically include task
evaluation and instructional demands (Ames, 1992). The classroom structures that have
been found to increase motivation and promote mastery learning are ―the design of tasks
and learning activities, evaluation practices and use of rewards, and distribution of
authority or responsibility.‖ (Ames, 1992, p. 263). Designing tasks, learning activities
and evaluation practices to allow for diversity in process and product has been found to
47
reduce peer comparison among students and differential treatment by teachers (Ames,
1992).
By examining school climate, researchers move beyond cognitive, motivational,
and familial characteristics of students to explore academic achievement. This additional
understanding of the roles of social interactions between teachers and students in the
school context may provide opportunities for interventions with school personnel to
improve academic achievement.
When considering a student’s perception of the classroom, research has shown
that there is variation both within classrooms and within students (Marshall & Weinstien,
1986). Marshall and Weinstein documented significant within-class differences in student
perception of autonomy and the level of work in the classroom (1986). Students’
perceptions of themselves and of their classroom are different because of both past
experiences and the way they interpret interactions between themselves and the teacher.
Ames, points out quite clearly, ―Thus to predict and examine motivated cognition, affect,
and behavior of a student, it is necessary to attend to how that student perceives and gives
meaning to classroom experiences.‖ (1992, p.267). Student perceptions of the level of
support and teacher expectations may explain reasons for engagement and offer
opportunities for interventions at an individual perception level once differences have
been noted.
The importance of school climate for racially diverse students has been found to
be significant (McMahon, Wernsman, & Rose, 2009; Stewart, 2008; McKown &
Weinstein, 2008; McKown & Weinstein, 2002). McMahon, et al., (2009) found
significant positive relationships between perceptions of school belonging, satisfaction,
48
and cohesiveness on language arts self-efficacy for racially diverse fourth and fifth
graders (N=149). In this study, school belonging had the strongest influence (β = .19)
when all measures of school climate (satisfaction, cohesion, difficulty, friction, and
competitiveness) were included in a model to predict language arts self-efficacy. On an
individual model level, the significant positive relationship between language arts selfefficacy and school belonging (.22) show an interaction between factors of motivation
and students’ perception of school as a safe, welcoming place.
Stewart (2008) used the National Educational Longitudinal Study (NELS) 1990
data to investigate school attachment and cohesion for African American tenth graders (N
= 1,238). School attachment was measured as the extent to which students felt positive
about interactions with teachers and other students. School cohesion measured students’
feelings about whether teachers and other students care about each other and the extent to
which students from different ethnicities interacted with one another. School cohesion
was significantly, positively correlated with grade point averages (r = .14). School
attachment was a significant predictor of GPA (t = 5.50) in a model that included
attachment, school commitment, school involvement, positive peers, parent-child
discussion, parental school involvement, gender, family structure, and family SES. In a
model of school structural variables, school cohesion was the only statistically significant
predictor (b = .014, t = 3.50) after controlling for individual level variables. These studies
show that school climate may be perceived differently for racially diverse students and
may have a stronger relationship to academic achievement.
49
Teacher Support
The situative nature of aptitude theory to explain performance requires a close
examination of the students’ perception of the learning environment. The high school
experience is quite different from college or life in the adult world. It is important to
conduct research in this area to help educators and parents understand the roles of
teachers and the school environment in influencing academic achievement.
Studies have shown that students’ perceptions of teacher expectations and
attitudes have a significant impact on student achievement (McKown & Weinstein, 2008;
Suldo, et al., 2008; McKown & Weinstein, 2003; McKown & Weinstein, 2002; Sanders,
1997). Teachers who hold high expectations and challenge their students while providing
support and encouragement motivate students to higher achievement. Teacher referrals
are often the first step in the process of identifying students for gifted or remedial learner
programs. As such, the teacher’s attitudes and understanding of African American
learners may play a pivotal role in selecting these students for special programs.
Way, Reddy, and Rhodes (2007) examined sixth grade students’ (N=1,451)
perceptions of school climate and teacher support to explore relationships between
students’ perception of school climate and psychosocial outcomes of behavior problems,
self-esteem, and depressive symptoms. This study found significant relationships
(medium effect size) between the intercepts and slopes of perceived teacher support and
measures of well being. Large effect sizes were reported for the slopes of perceived
teacher support and the slopes of problem behavior and depressive symptoms. There
were no significant differences found for gender or ethnicity. However, the sample was
91% European American. Students who reported more behavior problems in school also
50
had lower teacher support intercepts initially and the trajectory showed a negative slope
which indicates that as students with behavior problems progressed through the school
year, they perceived less teacher support.
Konstantopoulos (2009), evaluated the effects of teachers on early academic
achievement (K-3). Each grade had over 6,000 students and over 300 teachers participate.
The student groups were 33% minority. The main effect of belonging to a minority group
had a statistically significant negative relationship with mathematics achievement (-.229 -.413). Prior year effective teachers were positively significant for first and second grade
math achievement. Although the teacher effect * minority interaction was not significant,
there was a significant negative teacher * minority interaction for reading achievement in
first grade (-.169). There were significant main effects for minority status on reading
achievement in second and third grade (-.189, -.223).
Teachers provide scaffolding to students to help them learn and accomplish those
tasks that are beyond the students’ capabilities while working alone. Therefore, the role
of the teacher is critical for influencing students’ development based on the amount of
scaffolding provided. Teachers who hold a cultural deficit model for African American
students may not believe that these students are capable of high academic achievement.
―When children come to understand that others endorse stereotypic beliefs, they gain an
insight into others’ social motives that profoundly affects their relationship to other
individuals, social settings, and society.‖ (McKown & Weinstein, 2003, p. 498).
McKown and Weinstein (2002), sought to understand the moderating role of
ethnicity in teacher expectancy effects. The study had 30 teachers and 561 students
grades 3-5 (n= 228 Black, n = 231 White) participate. In the fall teachers were asked to
51
rank order students in terms of expected year-end achievement in reading and math based
on the previous year’s achievement results. This ranking was compared to students’
actual current year-end performance using hierarchical linear modeling. McKown and
Weinstein found that teachers consistently underestimated the performance of African
American students compared to European American students (2002). The researchers
also found that in third and fifth grade, African American students were statistically
significantly more likely to perform at levels that matched teacher underestimates of
performance. In this study, the African American students demonstrated that they were
adversely affected by teacher underestimates of their ability whereas European American
students benefited from the teacher overestimates of their ability.
This study highlights the need to recognize that African American students may
be more sensitive to perceptions of low teacher expectations. It is important to examine
the relationships among perceived school support, awareness of negative stereotypes,
motivation, and academic achievement to better understand the roles of teachers and the
school for African American students.
In a follow-up to this study, McKown and Weinstein sought to examine the
development of stereotype consciousness, or negative public regard, in 202 students of
heterogeneous ethnicity and ages 6 - 10 (2003). The researchers found that children from
ethnic minority groups perceived broadly held stereotypes at a younger age than
Caucasian students. Eighty percent of the ethnically diverse students at age 10 were
aware of negative stereotypes compared to 63% of Caucasian students. This finding aids
the understanding of identity development by illustrating that at a young age, children are
aware that they are different from those of the majority culture and membership in a
52
minority ethnic group may render them susceptible to others’ preconceived notions. This
study is also important for schools because it shows that children as young as six may be
aware of racial stereotypes held by others. Interactions with teachers and administrators
can serve to confirm the perceived negative public regard or to provide counter examples
to previous experiences and reinforce that while ethnic differences are recognized and
respected, the student is seen as an individual and teachers hold high expectations of all
students.
Measuring School Support
The Inventory of School Climate – Student Version (ISC-S) is an instrument used
to measure student perceptions of the school context (Brand, Felner, Shim, Seitsinger,&
Dumas, 2003). The initial instrument was created from 125 items that had been identified
in the literature as important to student adjustment (Brand, et al., 2003). These items were
reviewed by 1,000 students to determine the ease of readability and the question
comprehension. Following the pilot study, exploratory and confirmatory factor analysis
was conducted over two years. The samples for this analysis consisted of 16,600 students
in 42 middle schools. The ethnicity of the participants was not included in the study
description. The samples were randomly split into two groups; one was used for the
exploratory factor analysis, and one was used for confirmatory factor analysis. Items
were eliminated from the instrument if: they did not have similar factor loadings in both
subsamples in both years, they had factor loadings less than .325, or if they loaded on
more than one factor greater than .35 (Brand, et al., 2003). The results of this study led to
a revised survey with 61 items. Following the initial analysis, a 10 dimensional model
comprised of: Disciplinary Harshness, Negative Peer Interactions, Positive Peer
53
Interactions, Structure and Clarity of Rules and Expectations, Student Commitment to
Achievement, Teacher Support, Instructional Innovation, Student Participation in
Decision Making, Support for Cultural Pluralism, and Safety Problems was used to assess
the model fit to the data. Results from this analysis were a further reduction of 11 items
due to low factor loadings on the target factor and further refinement of the instrument.
The second study conducted by Brand, et al., (2003) was aimed at confirming the
structure of the scales and establishing reliability in a larger and more diverse sample of
schools. This study took place over three years and involved 161,000 students from 300
schools in 16 states. The participants in this study were 44% from minority racial-ethnic
groups, and 46% participated in free/reduced lunch. This large, diverse sample afforded
the researchers the opportunity to test the appropriateness of the instrument for diverse
students. Cronbach’s alpha for the subscales were as follows: Teacher Support (.76),
Clarity of Rules and Expectations (.74), Student Commitment to Academic Achievement
(.81), Negative Peer Interactions (.73), Positive Peer Interactions (.70), Disciplinary
Harshness (.67), Participation in Decision Making (.70), Innovation (.63), Support for
Cultural Pluralism (.68), and Safety Problems (.71). Correlations were computed between
the subscale scores across year 1 and year 2 to assess the stability of the climate ratings in
each building. The correlations of year one had a median r = .76 and year two had a
median r = .52 which indicates that the scores within each school were reasonably stable
across different cohorts of students. The samples were divided by gender, race, gradelevel and participating in free/reduced lunch to determine the reliability of the instrument
for different groups (Brand, et al., 2003). Cronbach’s alpha for each subscale for each
group were above .70. Factor loadings were examined by group and no substantial
54
differences were found for ethnicity or lunch status. Some small differences were found
for gender however the factor loadings differed by .10 or less. The researchers also
evaluated the subgroups at the building level using hierarchical linear modeling and did
not find significant differences among groups for ratings on the subscales.
The third study sought to understand how differences between schools on the
ISC-S were related to academic achievement and other student adjustment indicators
(Brand, et al., 2003). This study was conducted in conjunction with the previous study
and asked participants to provide additional information on ―academic efficacy,
delinquency, drug use attitudes – behavior, self-esteem, anxiety, and depression‖ (Brand,
et al., 2003, p. 577). The additional information was used to understand the predictive
validity of the ISC-S using school-level climate subscales. Hierarchical linear modeling
was used to test the subscales’ relationships to academic achievement and adjustment,
behavior problems and substance use, socioemotional adjustment, and minority and
majority students’ perceptions of school climate and adjustment. The strongest predictor
of academic achievement was Student Commitment to Academic Achievement. Other
subscales that showed consistent relationships with academic adjustment were Teacher
Support, Structure, Positive Peer Interactions, and Instructional Innovation (Brand, et al.,
2003). Support for Cultural Pluralism showed a consistent, positive relationship with
reported self-expectations and academic aspirations, which suggests that perceived
support for diversity may be an important aspect in motivation for academic
achievement. As one of the first scales to measure the school environment’s support for
diverse students, the ISC-S showed that minority students in schools that were rated by
55
students as having high levels of Support for Cultural Pluralism had higher academic
expectations and lower levels of delinquency and substance use.
Summary
Most of the literature on the psychosocial aspects of academic achievement
investigates each component individually. Researchers focus on their areas of interest –
motivation, cognitive ability, and other characteristics. Most of these studies did not
include participant racial information or were mainly conducted with White, European
American participants. The lack of research on the relationship among cognitive ability,
and motivation characteristics for African American students makes generalizations to
this group difficult.
African Americans have a qualitatively different experience in school and the
community than their White counterparts. The phenotypic differences of this group are
present from birth and may influence their self-concept and perceptions of those around
them. Racial identity seeks to understand the salience of race for individuals and the
meaning African Americans attach to the role of being Black in a predominantly White
society. Racial identity has typically been examined in conjunction with academic
achievement and thus other components of the psychosocial development are ignored.
This study will extend prior theories of aptitude development by investigating the
influence of racial identity factors as important aptitudes for academic learning.
Cronbach’s initial conceptualization of aptitude-interaction changed the way that
treatment affects were interpreted (1957). Snow (1989), Ackerman (1996), and Lubinski
(2000) recognized that different combinations of affective, conative and cognitive traits
interacted and enabled individuals to be more or less successful in different situations.
56
This research added a new factor to the concept, namely racial identity. If the other three
components are thought to interact with one another within a situation, adding another
factor which also develops as an individual interacts within a situation is reasonable to
extend the conceptualization.
Accordingly, this research considered the following questions to help understand
the role of racial identity:
1. What are the relationships between the racial identity factors (centrality, private
regard, and public regard) and perceived support?
2. What are the relationships between the racial identity factors (centrality, private
regard, and public regard) and intrinsic motivation?
3. Do racial identity factors moderate the relationship between perceived support
and motivation?
4. Do racial identity factors (centrality, private regard, and public regard) moderate
the relationship between academic intrinsic motivation and cumulative grade
point average after controlling for 6th grade standardized test scores?
5. Do the racial identity factors (centrality, private regard, and public regard)
moderate the relationship between academic intrinsic motivation and the number
of Advanced placement courses taken after controlling for 6th grade standardized
test scores?
6. Do racial identity factors (centrality, public regard, and public regard) moderate
the relationship between perceived school support and cumulative grade point
average after controlling for 6th grade standardized test scores?
7. Do the racial identity factors (centrality, private regard, and public regard)
moderate the relationship between perceived school support and the number of
57
advanced placement courses taken after controlling for 6th grade standardized test
scores?
58
CHAPTER 3 METHODS
Participants and Sampling
The purpose of this study was to understand the relationship among the factors of
racial identity, prior academic achievement, academic motivation, and perceived school
support in the prediction of academic success for African American students. Academic
success will be measured by 11th and 12th grade cumulative grade point average (GPA)
and the number of advanced placement courses taken. Predictors included measures of
racial identity, academic intrinsic motivation, perceived school support, and achievement
in elementary school. Participants who identified themselves as African American and
were currently enrolled in 11th and 12th grade were included in this study.
Due to the large number of required participants, this study was conducted in a
geographical area that has large student bodies. Two high schools in a large, Midwestern
metropolitan area agreed to participate in the study. This district has a history of students
taking a high number of honors courses. The school demographics are summarized in
Table 1.
59
Table 3.1.
Key Demographics of Schools Participating in the Study
Measure
School A
School B
Total Population
2,124
1,528
African American
32%
28%
Caucasian
34%
29%
Asian American
28%
33%
Hispanic
4%
9%
Native American
1%
1%
Eligible for Free or Reduced Lunch
58%
72%
African American Graduation Rate
56%
76%
Number of honors courses passed by
African American students
432
302
Students who took the ACT
64%
52%
Determination of the Sample
A letter describing the study and a parental consent form were mailed to a 233
11th grade students from the school district office. Students were given two weeks to
respond by mailing the signed consent forms back to the researcher in a pre-addressed,
stamped envelope. As a follow up measure, the school counselors at each school redistributed the forms to 11th grade students and passed out forms to 12th grade students
and asked students to return the signed consent forms to them. A total of 84 students
returned the consent forms and were administered the survey. The district provided the
researcher with the 6th grade reading and math test scores on the Standford Achievement
60
10 test, current GPA, and number of honors and number of advanced placement courses
taken by the student for each student who returned a signed consent form.
Students who returned consent forms were called into a conference room in
groups of 10 to 15 where the researcher briefly explained the survey. Students were told
that they could skip any questions that they did not feel comfortable answering and that
they could stop participating at any time. Students were encouraged to ask questions
about the survey or for clarification on any items. At the end of the survey administration,
the researcher drew one name from the participants at each school and that individual
received a $45 Visa gift card. The drawing was held to thank students for their time and
effort in completing the survey.
A one-hour survey session was needed for the principal investigator to administer
the questionnaire to the students. The instruments were numbered and a record was kept
with individual names and the survey numbers in order to match individual student test
scores and grade point averages with survey results. However, there was no other
identifying information on the instrument. After the data were entered into a spreadsheet
for analyses, the record connecting student names and survey numbers was destroyed.
IRB and Plan to Maintain Confidentiality
This study was approved by The University of Iowa Institutional Review Board.
There were minimal risks to the participants. The short survey asked their perceptions of
their own motivation, affective characteristics, racial identity, and perceptions of school
climate. The only foreseeable possible risk was feeling uncomfortable when asked
questions about how they feel about themselves or their school experience. The
participants may have benefited by examining how they felt regarding their performance
61
in school and the level of support they felt that they are receiving in their classroom. All
required steps such as providing confidentiality, explaining that they may end their
participation at any time without penalty, and fully explaining the purpose of the research
were taken to insure appropriate minimization of risks to participants.
Data was stored on a password protected computer that was not a part of a
network. The computer was maintained in a home office that was only accessible by the
principal investigator. Hard copy files were maintained in a locked file cabinet and the
record that links participant names to instrument identification numbers was destroyed
when the data was entered into spreadsheets.
Instruments
In order to answer the research questions, it is important to have quality measures
of each of the factors. Accordingly, this study required the use of three standardized
instruments to assess the key components of interest: academic intrinsic motivation (15
questions), racial identity (17 questions), and perceived school support (12 questions).
The survey instrument also asked five demographic questions.
Academic Intrinsic Motivation
The Academic Motivation Scale (AMS) –English version was developed based on
the self-determination theory of motivation (Vallerand, et al., 1992). The questionnaire
asks students to respond to the question ―Why do you go to school?‖ using a seven-point
Likert scale that indicates agreement with various responses. The original instrument was
created in French and consisted of 28 items and seven subscales. The subscales were:
intrinsic motivation to know, intrinsic motivation toward accomplishments, intrinsic
motivation to experience stimulation, external regulation, introjected regulation,
62
identification, and amotivation. Each subscale consisted of four items. The scale was
translated into English following the preferred approach of parallel-back translation
where the original instrument is translated into English by a bilingual person. A second
bilingual person then translated the English version back into French. Agreement
between the original document and the document translated back into French is used as
the measure of appropriateness of the translated instrument. Following the translation, the
items were assessed by a committee to determine the appropriateness of the translation.
Once agreement had been reached on the 28 items, a pilot-test was performed with 10
college students to assess the meaning and clarity of the items.
This study used the three subscales that measure intrinsic motivation. Intrinsic
motivation is a key component for understanding why people with similar cognitive
ability may show differences in academic performance. Thus, questions regarding the
overlap of subscales of intrinsic and extrinsic motivation of the AMS will not be pertinent
in this investigation.
Racial Identity
Racial identity was measured using the Multi-Dimensional Inventory of Black
Identity (MIBI) (Sellers, et al., 1998). As developmental theories of ethnic identity
continue to be refined and revised, researchers have begun to move away from the stage
theory of ethnic identity development that was originally proposed by Cross and Phinney.
The MIBI is a measure of racial identity that is not focused on stages but accepts that
development takes place on a continuum. The MIBI assesses Black identity along three
dimensions: centrality, regard, and ideology. The MIBI was originally constructed using
items from existing scales on African American racial and ethnic identity, as well as
63
social identity if they met the criteria for the conceptual framework of centrality, regard,
and ideology. Students are asked to respond using a seven-point Likert scale to the extent
that they agree with the statements given in the measure.
The Multidimensional Inventory of Black Identity (MIBI) was developed by
Sellers, Rowley, Chavous, Shelton and Smith in 1997. The original instrument consisted
of 71 questions based on the Multidimensional Model of Racial Identity. (Sellers, et al.,
1997). The model proposes that racial identity can be measured among four main areas:
Salience, Centrality, Regard, and Ideology. Salience refers to the situativity of racial
identity and as such varies with the context. This aspect of racial identity was not
included in the MIBI due to its transitory nature. Centrality refers to the importance raters
place on identifying themselves by race. The Multidimensional Model of Racial Identity
was developed using theories of hierarchical identity development. As such, it
acknowledges that racial identity may vary in centrality depending on the situation and
the other identity roles that an individual may play. Regard consists of two subscales,
private and public regard. This aspect refers to an individual’s judgment regarding how
others perceive Black people and how the individual feels about being Black. The
Ideology subscales measure individuals’ beliefs regarding how members of the Black
community should conduct themselves as far as interracial relationships, economics, and
living in a multiracial society. These beliefs were further categorized into four different
attitudes: Assimilationist, Humanist, Nationalist, and Oppressed Minority. An important
difference between the MIBI and other measures is that the MIBI does not combine
scores into one overall racial identity score. Also, the MIBI allows different components
64
of racial identity to be operationalized to determine the specific components that may be
moderating relationships between other factors.
The original instrument consisted of 71 questions that attempted to capture the
three stable dimensions of racial identity: Centrality, Regard, and Ideology. The centrality
subscale consisted of 10 items. The private regard subscale consisted of seven items. The
public regard subscale consisted of four items. Private and Public regard were combined
to form one scale labeled regard. The Ideology subscale consisted of 50 items.
Consistent with recent studies (Chavous, et al., 2003; Harper & Tuckman, 2006)
the ideology subsection of the MIBI will not be used in data collection. Due to the age of
the participants in this study and the areas of interest, only the Centrality, Public and
Private Regard subscales of the MIBI will be used. Although the Ideology subscale may
provide other important information, it is beyond the scope of this study to include these
questions. Furthermore, due to the limited experience that students may have had in
participating in the larger society, many of the ideological questions may be beyond the
scope of their understanding.
Perceived School Support
School climate was assessed to determine the students’ perceptions of how
supportive their school is in terms of promoting academic achievement and multicultural
awareness. This study utilized the Inventory of School Climate (ISC) – Student subscales
of Teacher Support and Support For Cultural Pluralism by Brand, et al.,(2003). The ISC
was created in an effort to provide an instrument that would measure the school
environment for middle and high school students. Prior to the creation of this instrument,
most scales had measured school climate at the elementary level where students typically
65
spend the entire school day with one teacher. Brand et al. recognized that such an
instrument would not be suitable for students who change classes throughout the day and
therefore sought to create a measure that would assess school climate at the school level
rather than the class level. The researchers conducted three studies to assess the
appropriateness, reliability, and validity of the instrument.
Prior Academic Achievement
Measures of academic achievement prior to entry to high school were obtained
from sixth grade scores on the Standford Achievement Test 10. Subsection scores for
reading and math were used to create composite measures. Current school grade point
average and number of honors courses taken were used to measure achievement in 11th
and 12th grade.
Data Analysis
Cronbach’s alpha and item inter-correlations among all scales were examined to
ensure that the instruments are functioning as expected and to establish construct validity.
Descriptive measures such as means and standard deviations were reported for each of
the variables of interest. The data from the MIBI were reported as scores on Centrality,
Private Regard, and Public Regard. Sixth grade standardized reading and math test scores
were combined to form a composite score for prior academic achievement.
First, analyses explored the relationships among the racial identity factors (racial
centrality, public regard, and private regard), academic intrinsic motivation, and
perceived school support among African American students. Initial analysis was
conducted to determine whether racial identity factors interact with other components of
the model. Correlational analysis was used to determine the extent of relationships
66
between academic intrinsic motivation, perceived school support, and the racial identity
factors among African American students. Regression analysis was conducted to
determine variables which add incremental validity to the prediction of academic
achievement as measured by grade point average and number of honors courses taken
beyond 6th grade standardized test scores.
The following questions were answered by examining the correlations among the
components of racial identity, perceived school support, and intrinsic motivation.
Significant correlations will be identified.
1.
What are the relationships between the racial identity factors (Centrality,
Public Regard, and Private Regard) and perceived support among 11 th and 12th
grade African American students?
2.
What are the relationships between the racial identity factors (Centrality,
Public Regard, and Private Regard) and intrinsic motivation among 11 th and 12th
grade African American students?
Second, the effect of students’ perceptions of social context, teacher attitudes, and
expectations and school climate, has been shown to influence academic achievement
(Ford, Grantham, Whiting, 2008; Milner & Ford, 2005). Stereotype threat, which has
been defined as ―being at risk of confirming as self-characteristic, a negative stereotype
about one’s group‖ (Steele & Aronoson, 1995, p.797), has been shown to extend beyond
testing conditions (Steele, 1997). Students may perceive teachers’ lower expectations and
respond accordingly as early as fifth grade (McKown &Weinstein, 2003). Thus, the
students who show the potential for advanced achievement may be influenced positively
67
or negatively by their experiences with teachers and the teachers’ expectations. The third
research question was answered by entering data simultaneously in three blocks in a
hierarchical regression model with intrinsic motivation as the dependent variable. Block
one contained the main effects of perceived support. Block two contained each racial
identity factor (centrality, private regard, public regard). Block three contained the twoway interactions of each of the racial identity factors with perceived support. The analysis
was initially run with all three blocks. If no significant interaction was found within the
third block, it was dropped from the model. The process was repeated in an attempt to
find the most parsimonious model to explain the relationship. When significance was
found, further tests were run to help clarify interpretations of the relationships.
3.
Do racial identity factors moderate the relationship between perceived
support and motivation?
The final stage of analysis investigated each of the racial identity factors
individually with the other predictor and outcome variables. Given the large number of
variables and interactions, analyses was be performed separately on each of the two
dependent variables: grade point average (GPA) and number of advanced placement
courses taken. For each analysis, a composite measure (total reading and math scores
combined) of 6th grade achievement was entered into the regression to control for
differences in academic readiness at high school entry. The main effects of this study
were racial identity factors, motivation, perceived school support, and cognitive ability.
This study sought to improve the prediction of academic achievement by understanding
68
the relationships among these variables. Accordingly, the following questions were
investigated:
4.
Do racial identity factors moderate the relationship between academic
intrinsic motivation and grade point average after controlling for 6 th grade
academic achievement?
5.
Do racial identity factors moderate the relationship between academic
intrinsic motivation and the number of Advanced placement courses taken after
controlling for 6th grade academic achievement?
6.
Do racial identity factors moderate the relationship between perceived
school support and grade point average after controlling for 6 th grade academic
achievement?
7.
Do racial identity factors moderate the relationship between perceived
school support and the number of advanced placement courses taken after
controlling for 6th grade academic achievement?
These questions were investigated using hierarchical regression analysis. Due to
the large number of variables involved, analyses were run separately for each dependent
variable (grade point average and number of advanced placement courses taken).
Furthermore, the racial components were tested individually to investigate any
interactions between the racial component and either academic intrinsic motivation or
perceived school support. Each model was stated in terms of variance accounted for as
69
well as improved prediction. The models consisted of four blocks. The first block
contained a composite measure of 6th grade academic achievement (test score) to control
for differences in readiness as the students begin their high school careers. The second
block contained one of the racial identity variables. The third block contained one of the
two remaining predictors (academic intrinsic motivation or perceived school support).
The final block of the model contained an interaction term of the racial identity factor in
the second block and the predictor used in block three. Analyses were run for each
outcome measure, each racial identity factor and each predictor for a total of twelve
models.
70
CHAPTER 4 RESULTS
The purpose of this study was to explore relationships among racial identity
factors, perceived school support, academic motivation, and academic achievement for
African American 11th and 12th grade students. The final sample consisted of 56 11th and
12th grade students from two schools in the metropolitan mid-west. The participants
completed a demographic questionnaire, plus a self-report survey on racial identity
factors, perceived school support, and academic intrinsic motivation. Measures of
cumulative grade point average, number of advanced placement courses taken, and sixth
grade standardized test performance on reading and math were obtained from school
records. Preliminary statistical analyses were conducted for the purposes of describing
the sample, and confirming whether combining the two schools into one sample was
justified. The research questions were then answered in a series of bi-variate correlation
and hierarchical linear regression analysis.
Missing Data
Table F1 summarizes information concerning the total number of consent forms
distributed to student’s families (233), the number of completed surveys (84), and the
number of surveys deleted from analyses due to missing sixth grade achievement test
scores (26). The final sample consisted of 56 11th and 12th grade students. The students
were allowed to skip any questions that they did not want to answer and as such 11
responses out of a total of 2,296 or .04% were left blank. Scores were imputed for the
missing data by averaging the individual’s subscale scores and using that average as the
missing score.
71
Preliminary Statistical Analyses
In the demographic section of the survey, students reported information on race,
father’s education level and mother’s education level. The schools provided information
on students’ sex, and grade. Table F2 summarizes the demographic profiles of students
who participated in the present study. Demographic characteristics of students in the two
schools were compared to determine if combining the data would be appropriate (Table
F3). Parents’ education level was used as one indication of parental socioeconomic status
as details regarding family income were not obtained. In addition to demographic
characteristics, measures of students’ achievement were also compared. Independent
samples t-tests for equality of means analyses were performed on the groups to compare
each of the variables (Table F4). No statistically significant differences were detected;
therefore it was deemed appropriate to examine the group as a whole. These scores were
compared to national norms. Overall, students in this study scored between the 48 th and
64th percentile nationally (national mean = 1298) on the Standford Achievement Test
Version 10. Given that the percentages of students from different grades varied for the
two groups of students, it was deemed appropriate to combine the students from the
schools into a single sample.
Table F5 reports summary statistics for the self-report measures (racial centrality,
private regard, public regard, perceived school support, and academic intrinsic
motivation). The average racial centrality score for students in the sample was 15.40
(SDCENT = 2.78) on a scale that ranged from 5 to 25. The average private regard score for
students in the sample was 26.25 (SDPRI = 3.40) on a scale that ranged from 6 to 30. The
average public regard score for students in the sample was 16.26 (SD PUB = 3.61) on a
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scale that ranged from 6 to 30. The average perceived school support score for students in
the sample was 42.46 (SDSUPP = 7.27) on a scale that ranged from 12 to 60. The average
academic intrinsic motivation score for students in the sample was 50.35 (SD MOT = 4.52)
on a scale that ranged from 12 to 60.
Table F5 also reports the Cronbach’s coefficient alpha (α), for each of the selfreport measures (racial centrality, private regard, public regard, perceived school support,
and academic intrinsic motivation) as a measure of internal consistency reliability based
on the sample of students in the present study, and compared to the several studies
previously cited in Chapter 2. All of the sample measures for this study had Cronbach’s
alpha coefficient above 0.60.
Table F6 reports the Pearson product moment (zero-order) correlations and
disattenuated correlations between scores on the self-report measures. A disattenuated
correlation is a Pearson correlation that is divided by the square root of the product of the
reliabilities of the variables being correlated. Thus, a disattenuated correlation may be
conceptualized as a Pearson correlation that has been corrected for the measurement error
in the scores; in effect it estimates the correlation between the true scores on the two
measures. A disattenuated correlation close to 1 indicates that two scores are essentially
measuring the same construct. The largest disattenuated correlation was between racial
centrality and private regard (.461) which is about 21.3% shared true score variance. The
low disattenuated correlations suggest very little overlap between the constructs being
measured by the self-report questionnaire.
Although statistically significant Pearson correlations were found between racial
centrality and private regard, public regard and private regard, and perceived school
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support and academic intrinsic motivation, none of the correlations were over .32 which
indicates that although they are statistically significant, the correlation may not be
practically significant. It is interesting to note that, although not statistically significant,
racial centrality was negatively correlated with both perceived school support and
academic intrinsic motivation. This suggests that as racial centrality increases, students’
perceptions of school support and academic intrinsic motivation may decrease.
Table F4 shows the ranges, averages, and standard deviations for cumulative
grade point average, number of advanced placement courses taken, and 6th grade
standardized test scores.
Statistical Analyses Used to Answer the Primary Research Questions
The Relationships Between Racial Identity
Factors and Perceived Support
The first research question was:
1. What are the relationships between the racial identity factors (centrality, public
regard, and private regard) and perceived support among 11th and 12th grade
African American students?
This question was answered by examining the correlations among the variables in Table
F6. There were no significant relationships between the racial identity factors and
perceived support.
The Relationships Between Racial Identity
Factors and Academic Intrinsic Motivation
The second research question was:
74
2. What are the relationships between the racial identity factors (centrality, public
regard, and private regard) and academic intrinsic motivation among 11th and 12th
grade African American students?
This question was addressed by examining the correlations in Table F6. There were no
statistically significant relationships between any of the racial identity factors and
academic intrinsic motivation.
The Relationships Among Racial Identity Factors,
Perceived Support and Academic Intrinsic Motivation
The third research question was:
3.
Do racial identity factors moderate the relationship between perceived support
and motivation among 11th and 12th grade African American students?
The regression model for the outcome measure of academic intrinsic motivation is shown
in Table F7. The full model that included separate racial identity factors (centrality,
private regard, and public regard), perceived support and the three interactions, used a
partial F test to determine the statistical significance of adding the interaction terms to
model 3.2 which only included the main effects of the racial identity factors and
perceived school support. As shown in the last row of table F7, the F[MOT|SUP, CEN,
PRI, PUB, SUP*CEN, SUP*PRI, SUP*PUB] = 1.546 (p = 0.215) which indicated that
the null hypothesis should be retained. The addition of the interaction terms to model 3.2
did contribute to academic intrinsic motivation beyond the contribution of the main
effects of the variables.
Two simpler models were also tested. Model 3.2 used a partial F test to determine
if the addition of the racial identity factors would improve the prediction of academic
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intrinsic motivation significantly beyond the independent variable of perceived school
support. This model was not significant. Model 3.1 which only included perceived school
support had a statistically significant relationship with academic intrinsic motivation.
Specifically F[MOT|SUP] = 5.197 (p = .027).
Model 3.1 explained 9% of the variance in academic intrinsic motivation. The
addition of the racial identity variables (centrality, private regard, and public regard)
contributed another 2.8% of the variance. Together, the interactions of the racial identity
variables and perceived support, the main effects of each racial identity variable and
perceived support explained 19.4% of the variance in academic motivation which is more
than twice the variance explained by model 3.1.
Table F7 illustrates the magnitude and direction of the relationships among the
main effects of perceived school support and each of the racial identity variables, their
interactions and academic intrinsic motivation. A statistically significant interaction was
found between perceived school support and private regard B = 3.149 (p = .042). The
zero order correlation of the interaction term (perceived school support * private regard)
is .249 (Table F6) is considered to be a small effect size (0.1 < r < 0.2) by Cohen (1988).
One is also mindful that effect sizes must be considered with a detailed understanding of
the variables under investigation in order to make meaningful interpretations. The
positive zero order correlation of perceived support is .296 which is just below Cohen’s
criteria for a medium effect size (1988).
As a result of a statistically significant interaction between private regard and
perceived school support, reduced models including each interaction term individually
are presented in Table F8. Of the three models, only the one using the interaction term of
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private regard and perceived school support resulted in a statistically significant
improvement. Specifically, the addition of only one interaction term (private regard *
perceived school support) significantly improved model 3.4B over model 3.2 which only
included the main effects of perceived support and private regard F [MOT|SUP, CEN,
PRI , PRI * SUP] = 4.329 (p = .042). This result suggests that removing the nonsignificant interaction terms improved the model’s ability to predict motivation. The
overall reduced model was able to explain 15.8% of the variance in motivation which
while it is less than the full model of 19%, it is meaningfully different from the Model 3.2
that did not include any interaction terms.
A graph of the interaction of private regard and perceived support on academic
motivation is shown in Figure 1. It appears that differences in the levels of academic
intrinsic motivation are more pronounced for students with lower levels of perceived
school support. Private regard correlated positively with academic intrinsic motivation
for students with perceived school support less than one standard deviation below the
mean. The relationship between private regard and academic intrinsic motivation was not
significant for students at the mean or above one standard deviation for perceptions of
school support.
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Figure 1. Graph of interaction of Private Regard and Perceived School Support on
Academic Motivation
Interaction of Private Regard and Perceived Motivation on
Academic Intrinsic Motivation
Academic Intrinsic Motivation
56
54
52
50
Private
48
-1SD
Mean
46
+1SD
44
42
40
-2SD
-1SD
Mean
+1SD
Perceived School Support
+2SD
Thus, the answer to question three depends on the number of interaction terms
included in the model. If the full model, that included all three interaction terms, is used
then the answer is ―no‖; racial identity variables do not moderate the relationship
between perceived school support and academic intrinsic motivation. However, if the
models examine the relationship of each racial identity variable individually, then the
answer to question three is ―yes‖; private regard moderates the relationship between
perceived school support and academic intrinsic motivation. Specifically, the interactions
were significantly different from one another for those students above and below one
standard deviation on perceived support.
78
The Relationship Among Racial Identity Factors,
Academic Intrinsic Motivation and Outcome Measures
The fourth and fifth research questions explored the direction and magnitude of
the relationships among the racial identity factors (centrality, private regard, and public
regard), academic intrinsic motivation, and two outcome measures (grade point average
and number of Advanced placement courses taken). Specifically, the questions were:
4. Do racial identity factors moderate the relationship between academic intrinsic
motivation and grade point average after controlling for 6 th grade academic
achievement?
5. Do racial identity factors moderate the relationship between academic intrinsic
motivation and number of advanced placement courses taken after controlling for
6th grade academic achievement?
Question 4
The regression model for the outcome measure of grade point average is shown in
Table F11. The full model that included the separate factors and their interactions used a
partial F test to determine if the addition of the interaction terms between academic
motivation and each of the three racial identity factor (centrality, private regard, and
public regard) significantly improved the prediction of grade point average over model
4.3 that included only the main effects of sixth grade academic achievement, academic
intrinsic motivation, racial centrality, private regard, and public regard. Specifically, for
Model 4.4 F[CEN*MOT,PRI*MOT,PUB*MOT|6TH, MOT,CEN,PRI,PUB] = .135, (p =
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.939) indicating that the null hypothesis should be retained. The addition of the
interaction terms did not contribute to the prediction of grade point average.
Two simpler models were also tested. Model 4.3 used a partial F test to
determine if the addition of the racial identity factors would improve the prediction of
grade point average significantly beyond the independent variables of sixth grade
achievement and academic intrinsic motivation. This model was not statistically
significant. Similarly, model 4.2 used a partial F test to evaluate if academic intrinsic
motivation would significantly improve the model beyond sixth grade academic
achievement. This model also failed to provide statistically significant evidence that the
addition of academic motivation or racial identity factors improved the prediction of
grade point average beyond that of sixth grade achievement. Therefore, the answer to
research question four is ―no‖; the racial identity variables do not moderate the
relationship between academic motivation and grade point average after controlling for
sixth grade achievement.
The magnitude and direction of the relationships among sixth grade academic
achievement, academic intrinsic motivation, racial identity variables (centrality, private
regard, and public regard) and grade point average are show in Table F12. Only sixth
grade academic achievement was a significant predictor in the any of the models. The
strong correlation of r = .584 for sixth grade academic achievement meets Cohen’s
(1988) threshold for a large effect size (r >.5).
Question 5
Research question 5 was answered through hierarchical regression analysis shown
in Table F13. A partial F test was used to determine if the adding interaction terms
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between academic intrinsic and each of the three racial identity factors (centrality, private
regard, and public regard) to model 5.3 significantly improved the prediction of the
number of Advanced placement courses taken over model 5.3 that only included the main
effects of sixth grade achievement, academic intrinsic motivation, and each racial identity
factor. Specifically, for model 5.4, F[NAP, CEN*MOT, PRI*MOT,PUB*MOT|6TH,
MOT, CEN, PRI, PUB] = 5.185 (p = .004) indicating that the null hypothesis should be
rejected; that there was a statistically significant, linear relationship among the number of
advanced placement courses taken and one or more of the interaction terms between
academic motivation and each of the three racial identity factors.
The full model, 5.4 explained 43.9% of the variance in the number of advanced
placement courses taken which was significantly more than the 25.3% of the variance
explained by the main effects of sixth grade achievement, motivation, centrality, private
regard, and public regard. Therefore, the answer to research question five is ―yes‖; the
racial identity variables moderate the relationship between academic intrinsic motivation
and the number of Advanced placement courses students took.
Table F14 shows the direction and magnitude of the variables in the models 5.1 –
5.4. Significant interactions were found between racial centrality and academic intrinsic
motivation [standardized B = -3.827 (p = .025)] and between private regard and academic
intrinsic motivation [standardized B = -3.971 (p = .034)]. This suggests that the effects of
perceived racial centrality and private regard may have different relationships with
academic intrinsic motivation on the number of advanced courses students took.
Figures 2 and 3 show graphs of the interactions in the full model after controlling
for the effects of sixth grade achievement. These graphs were created by calculating the
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predicted number of advanced placement courses taken using sixth grade achievement.
The residuals between observed and predicted number of advanced placement courses
(AP) were used to plot the interactions to show the relationship between racial centrality,
private regard and motivation when sixth grade achievement was controlled. Both figures
show a similar pattern in which the differences in the number of AP courses taken are
more pronounced for students with lower levels of academic intrinsic motivation. The
standardized beta weights for racial centrality and private regard were quite similar (B
racial centrality = 4.077, B interaction of centrality and motivation = -4.247; B private
regard = 4.419, B interaction of private regard and motivation = -5.615) thus the lines are
nearly identical.
For students with lower levels of academic intrinsic motivation, racial centrality
correlated positively with number of courses taken. The relationship between racial
centrality and the number of advanced placement courses taken is significant for those
students one standard deviation below the mean on academic intrinsic motivation. It is
not significant for those students near or above the mean for motivation
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Standardized Predicted Number of AP
Courses Taken
Figure 2. Graph of Interaction between Centrality and Motivation on Number of
Advanced Placement Courses Taken
Interaction of Racial Centrality and Motivation on Number of
AP Courses Taken when 6th grade academic achievement is
controlled
1.5
1
0.5
0
-1SD Centrality
-0.5
Mean Centrality
+1SD Centrality
-1
-1.5
-2SD
-1SD
Mean
+1SD
+2SD
Motivation
Figure 3 also showed larger differences in the relationship between private regard
and the number of advanced placement courses taken for students with lower levels of
academic intrinsic motivation than for those with motivation above the mean. For
students with low levels of academic motivation, private regard correlated positively with
the number of advanced placement courses taken. Statistically significant differences in
the relationship between private regard and the number of advanced placement classes
taken were found for students with academic intrinsic motivation scoring at the mean and
below. The relationship was not significant for students with academic intrinsic
motivation levels above the mean.
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Figure 3. Graph of Interaction between Private Regard and Motivation on Number
of Advanced Placement Courses Taken
Interaction of Private Regard and Motivation on Number of AP
Courses Taken when 6th grade achievement is controlled
Standardized Predicted Number of AP Courses
1.5
1
0.5
0
-1SD Private Regard
Mean Private Regard
-0.5
+1SD Private Regard
-1
-1.5
-2SD
-1SD
Mean
+1SD
+2SD
Motivation
Question 5 Reduced Models
Model 5.5 examined each racial identity variable and its interaction with
academic motivation individually to test which variables significantly improved the
ability to predict the number of AP courses taken over model 5.2 which only included the
main effects of sixth grade achievement and academic intrinsic motivation. The reduced
models allow each variable to claim the common variance that is shared by all three
variables in the full model. Thus it is not surprising that each racial identity factor
interaction was significant. The variance explained by the individual models ranged from
27.7% - 36%. All of the models showed improvement over Model 5.2 which explained
21% of the variance. When the size and magnitude of the relationships in the reduced
models are compared to the full model which included all of the racial identity variables
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and their interactions, the standardized coefficients for the interaction terms increased
while the standardized beta for sixth-grade academic achievement decreased (TableF16).
It is interesting to note that all of the other standardized coefficients for main effects were
positive; however, the standardized coefficients for the interaction terms were negative
for each of the reduced models 5.5.
The answer to research question five is complex. When the full model that
included all of the interaction terms was tested, no statistically significant differences
were found which would suggest that the answer is ―no‖; racial identity variables do not
moderate the relationship between academic intrinsic motivation and the number of
advanced placement courses taken when all of the variables are included in the model.
However, racial centrality and private regard did have a statistically significant
interaction in the full model and each was significant in the reduced models. Therefore,
the answer to question five would be ―yes‖; when considered individually, racial identity
variables do moderate the relationship between academic intrinsic motivation and the
number of advanced placement courses taken. Specifically, racial centrality and private
regard have significant moderation effects.
The results of the regression analysis of these two research questions provide
interesting differences. In question four, sixth- grade academic achievement was the only
significant predictor of grade point average and academic intrinsic motivation did not
demonstrate a strong relationship with grade point average. Racial identity variables did
not moderate the relationship between academic intrinsic motivation and grade point
average. However, the lack of significant interactions could be reflective of the weak
relationship between academic intrinsic motivation and grade point average. This may
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explain why other variables, such as teacher subjectivity, extrinsic motivation, and
academic self-concept, may have more influential roles in grade point average than those
examined in the current study. The results of question five provided evidence that racial
identity variables moderated the relationship between academic intrinsic motivation and
the number of advanced placement courses taken by students. Thus it appears that the
role of academic intrinsic motivation may vary with different outcome measures.
The Relationship Among Racial Identity Factors,
Perceived Support and Outcome Measures
The final research questions explored the direction and magnitude of the
relationships among the racial identity factors (centrality, private regard, and public
regard), perceived school support and two outcome measures (grade point average and
number of advanced placement courses). Specifically, the questions were:
6. Do racial identity factors moderate the relationship between perceived school and
grade point average after controlling for 6th grade academic achievement?
7. Do racial identity factors moderate the relationship between perceived school
support and number of advanced placement courses taken after controlling for 6th
grade academic achievement?
Question 6
The regression model for the outcome measure of grade point average is shown in
Table F17. The full model (6.4) that included the separate racial identity factors and their
interactions with perceived school support used a partial F test to determine the statistical
significance of the addition of the interaction terms between perceived school support and
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each of the three racial identity factors (centrality, private regard, and public regard).
Specifically, for model 6.4 F[CEN*SUP, PRI*SUP, PUB*SUP|6TH, SUP, CEN, PRI,
PUB] = .880, (p = .458) indicating that the null hypothesis should be retained. The
addition of the interaction terms did not statistically contribute to the prediction of grade
point average.
Two simpler models were also tested. Model 6.3 used a partial F test to determine
if the addition of the racial identity factors would improve the prediction of grade point
average beyond the independent variables of sixth grade academic achievement and
perceived school support. This model was not statistically significant F[CEN, PRI,
PUB|6TH, SUP] = 2.255, (p = .093) indicating that the null hypothesis should be retained
for the alpha of .05. Model 6.2 used a partial F test to evaluate if perceived school support
would significantly improve the model beyond sixth grade academic achievement. This
model also failed to provide statistically significant evidence that the addition of
perceived school support improved the prediction of grade point average beyond that of
sixth grade achievement. Specifically, F[SUP|6TH] = 1.336 (p = .253). None of the
models were able to predict grade point average significantly better than sixth grade
achievement alone. Therefore, the answer to research question six is ―no‖; the racial
identity variables do not moderate the relationship between perceived school support and
grade point average after controlling for sixth grade achievement.
The direction and magnitude of the relationships among sixth grade academic
achievement, perceived school support, racial identity variables (centrality, private
regard, and public regard) are detailed in Table F18. In examining all of the models,
perceived school support has a statistically meaningful relationship with grade point
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average (p = .062) in model 6.3 however, the standardized beta in this model is .213
which is less than half of the standardized beta for sixth grade academic achievement
(B6TH = .548) and lower than the standardized beta for racial centrality (BCEN = .233, p =
.057). This suggests that perhaps perceived school support does not have an influential
relationship with grade point average.
Question 7
The regression model for the outcome measure of number of advanced placement
courses taken is shown in Table F19. The full model that included sixth grade academic
achievement, perceived school support, the separate racial identity factors and their
interactions used a partial F test to determine whether the addition of interaction terms
between the racial identity variables and perceived support significantly improved the
prediction of advanced placement courses taken over the prediction of model 7.3 which
included only the main effects. Specifically, this model tests the partial F[CEN*SUP,
PRI*SUP, PUB*SUP|6TH, SUP, CEN, PRI, PUB] = 1.071, p = .371. The results of this
test indicate that no significant difference exists and thus the null hypothesis should be
retained. The addition of the interaction terms did not statistically contribute to the
prediction of the number of advanced placement courses taken.
Two simpler models were also tested. Model 7.3 used a partial F test to determine
if the addition of the racial identity factors to model 7.2, which only included the
variables of perceived school support and sixth grade academic achievement, would
improve the prediction of the number advanced placement courses taken. The results of
this test F[CEN, PRI, PUB|6TH, SUP] = 1.307, p = .282. These results indicate that no
meaningful evidence exists that racial identity variables add to the prediction of the
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number of advanced placement courses that students take over the prediction given by
perceived school support and sixth grade academic achievement. Model 7.2 used a partial
F test to evaluate if perceived support would significantly improve the model beyond
sixth grade academic achievement. This model also failed to provide statistically
significant evidence. In examining all the models, only sixth grade academic achievement
significantly predicted the number of advanced placement courses students took. The full
model including interaction terms was able to explain 33.6% of the variance in the
number of advanced courses student took; however, sixth grade achievement alone was
able to explain 20.2% of the variance. The increase of 13.4% did not reach the level of
statistical significance in contribution. Therefore, the answer to research question seven is
―no‖; the racial identity variables do not moderate the relationship between perceived
school support and the number of advanced placement courses taken beyond sixth grade
academic achievement.
Examination of the regression and correlation coefficients for models 7.1 – 7.4 in
Table F20 shows that none of the interaction terms between perceived academic support
and racial identity variables were significant. In model 7.4, the full model, only sixth
grade academic achievement was significant B = .520 (p = .000). Perceived school
support has a significance of p = .065 in the full model. A comparison of the standardized
betas for perceived school support in model 7.4 (B = 2.338) to Model 7.3 (B = .226)
suggests that the racial identity variables may influence the relationship between
perceived school support and the number of advanced placement courses students took. A
similar phenomenon did not occur with sixth grade standardized academic achievement
(B = .520, Model 7.4 vs. B = .511, Model 7.3).
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Questions six and seven allowed the examination the relationship of perceived
school support, racial identity variables, and outcome measures of grade point average
and the number of advanced placement courses taken. In both questions, no significant
evidence was found that racial identity variables moderate the relationship between
perceived school support and the outcome measures. The zero-order correlation of
perceived school support and the outcome measures in both questions was quite low rSUP,
GPA =
.091, rSUP, NAP = .151. Thus the weak relationship between perceived school support,
grade point average, and number of advanced placement courses taken may be the main
influence in the lack of statistical significant relationships.
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CHAPTER 5 CONCLUSIONS AND DISCUSSION
Conclusions
The purpose of this study was to understand the relationships among components
of racial identity (centrality, private regard, and public regard), prior academic
achievement, academic intrinsic motivation, and perceived school support to predict
academic achievement, as measured by grade point average and number of Advanced
placement courses taken, for African American students. Correlations between the
variables were examined to determine the strength of the relationship among each
independent variable (see Table F4 for correlations among studied variables). While
statistically significant relationships were noted among racial centrality and private
regard, private regard and public regard, and perceived support and academic motivation,
these correlations may not be practically significant. It was interesting that none of the
racial identity variables had a significant relationship with perceived school support.
A model was created to understand the roles of perceived school support, racial
identity factors and academic intrinsic motivation. This model showed a significant
interaction between private regard and perceived school support in explaining academic
intrinsic motivation. Specifically, the students who had a lower perceived school support
showed different relationships between private regard and academic intrinsic motivation
than did students with mean or higher perceived school support.
Models were created to control for prior academic achievement and explore the
relationships among the racial identity factors, academic motivation, and perceived
school support to increase an understanding of the variance in academic achievement. A
total of four models were created to investigate whether racial identity variables
moderated the relationship between academic intrinsic motivation, perceived school
91
support, and the outcome measures of grade point average and number of Advanced
placement courses taken. The only statistically significant interactions were found in the
model that investigated racial identity factors and academic motivation to explain the
number of Advanced placement courses taken after controlling for prior academic
achievement. Significant interactions were found between academic intrinsic motivation
and racial centrality and between academic intrinsic motivation and private regard. An
examination of the interaction graphs showed that for students with lower levels of
academic intrinsic motivation, the racial identity variables of centrality and private regard
were positively correlated with the number of Advanced placement courses taken. For
students with academic motivation at the mean or higher, there were no statistically
significant differences in the relationships between racial centrality and the number of
courses taken.
There were no other statistically significant interactions, which suggests that
racial identity factors do not moderate the relationships between perceived school support
and grade point average or the number of Advanced placement courses taken, and
academic intrinsic motivation and grade point average, when prior academic achievement
has been controlled.
Discussion
Racial Identity Variables
The correlations of racial identity variables in this study were similar to those
found by other researchers using the Multidimensional Inventory of Black Identity
(Chavous, et al, 2003; Walsh, 2001; Sellers et al., 1998). In comparison, similar findings
of the relationship between private regard and academic intrinsic motivation have been
found in previous studies. Chavous, et al., (2003) found that Private regard had a
92
significant relationship with school attachment. The results of this study contrast with
those reported by Harper and Tuckman (2006) where students with low levels of racial
centrality, public regard, and private regard achieved significantly higher grade point
averages than students with high levels of the racial identity variables. One possible
explanation may be the addition of the control variables of sixth grade academic
achievement and the other independent variables of perceived school support and
academic intrinsic motivation. This study added to existing literature by highlighting the
importance of disentangling components of racial identity. In questions three and five, the
racial component interactions taken together were not significant however; when
examined individually, significant interactions existed. The importance of this finding
illustrates that combining racial identity into one overarching component (i.e. ―ethnic
identity‖ Phinney, 1989) may miss the subtle influences of individual factors such as
racial centrality, private regard or public regard. For example, students with lower levels
of academic motivation had higher achievement when they felt positive about being
Black and belonging to the Black community (private regard). When the three racial
factors were examined as one group, there were no strong relationships noted. If
researchers only looked at the single group, they would miss the importance of the
individual components.
Academic Intrinsic Motivation and Perceived School Support
When exploring the results of the study, it became apparent that the weak
relationships between academic intrinsic motivation and perceived school support with
the outcome measures limited the ability to determine the impact of racial identity
variables. In hindsight, a problem may have been the ways in which academic intrinsic
motivation and perceived school support were measured. Although both were measured
93
using existing, psychometrically sound instruments, they may not have been precise
enough to notice differences with these students. Academic intrinsic motivation was
measured using the Vallerand, et al., (1992) subscales of intrinsic motivation to
experience stimulation, intrinsic motivation to accomplish, and intrinsic motivation to
know. It is possible that limiting the variable to intrinsic motivation to accomplish would
better suit the needs of this study because the outcome measures were measures of
accomplishment (grade point average and number of Advanced placement courses
taken). However, Gagné and St. Père (2002) found similar results with no statistically
significant relationships reported between intrinsic motivation and academic achievement
once IQ was controlled. Their study examined both intrinsic and extrinsic motivation and
neither was able to contribute to the prediction of academic achievement beyond IQ
scores.
Perceived School Support was measured using the Brand, et al. (2003) subscales
of Teacher Support and Support for Cultural Pluralism. These two subscales may be
measuring two distinct constructs, one of a personal nature (teacher support) and one of
an overall administrative nature (support for cultural pluralism). In reality, students could
have opposing perceptions of teacher interactions and administrative support for diversity
which may be lost in combining the two scores. Thus, the small correlations between
these independent variables and the outcome measures may be reflecting a need for more
precise measurement.
In regard to prior research, McKown and Weinstien (2003) found a relationship
between perceived negative stereotypes (public regard) and a decrease in cognitive
performance. The current research study did not reveal any significant relationships
94
between perceived public regard and academic achievement. Negative public regard,
stereotypes, have received a great deal of attention in the literature (Steele, 1997; Steele
& Aronson, 1995), yet the students in this study did not report excessively negative
perceptions of public regard (Mean = 16, range = 24). Perhaps because the students of
this study did not perceive negative public perceptions, this aspect of racial identity did
not contribute significantly in any of the models.
Konstantopoulos (2009) found that there were no significant differences between
race and teacher effect on academic achievement; these appear to be similar to the
findings of this study. Perceived school support, comprised of teacher relationship and
administration support for cultural pluralism in this study, did not show significant
relationships with either grade point average or number of Advanced placement courses
taken. Konstantopoulos found that all students, regardless of race, gender or social class,
showed academic improvement when taught by effective teachers (2009). However, once
prior achievement was controlled, the teacher effects were not statistically significant.
The current study found a significant correlation between perceived school support and
academic motivation. However, when sixth grade achievement was controlled, there was
a very weak correlation between perceived school support and either grade point average
or number of advanced placement courses taken. Thus, prior achievement appears to be
such an important determinant of future performance that there is little variance left to be
accounted for by perceived school support.
Study Limitations
There are several limitations to this study that warrant caution when interpreting
the findings and attempting to generalize across settings. The size of this sample is quite
small which brings into question the power of the ability to detect significant effects.
95
Power is the probability of making a type II error or failing to reject a false null
hypothesis. Given that the effects of this study were in the small to medium range, using
a sample size of 56 lends a power of .75 for simple correlations. Thus, significant effects
for small correlations may exist and would have been detected if the sample size were
larger.
Another limitation of this study involves the use of self-report measures.
Although the instruments used have documented validity and reliability, participants may
have introduced possible bias through answering in socially desirable ways. Participants
were asked questions about their school which may limit the generalizability because the
responses are situationally specific. Not all schools are the same in terms of social
support therefore different settings may elicit different responses by the students. Even
among students attending the same school, respondents indicated variability in their
perceptions of school support which makes generalizing this relationship difficult.
The participants in this sample came from an urban school district in the Midwest
that was heterogeneous in the racial make-up of the school population. Thus, the student
experiences in terms of the racial factors could be quite different from those students who
live in large metropolitan areas or areas where the schools are more racially
homogeneous. Students who attend schools where the population is primarily African
American may experience the racial identity factors quite differently than students in the
study schools. Similarly, students who are among the few non-white students in a district
may have very different experiences of teacher support, strength of racial centrality, and
public regard. Although the school district in this study was quite large, it is not large in
comparison to those on either coast of the United States. Thus the size of the district may
96
influence the educational experiences of the participants in unique ways that would limit
the generalizability of the study.
The final area of concern regarding the generalizability of this study is the number
of Advanced placement courses taken by students. This district has a history of
promoting African American students participation in advanced placement courses. The
schools involved in the study have specific programs in place designed to support
students in taking advanced coursework beginning in the ninth grade. As such, students
in these districts may have access to more advanced placement courses and support to
help African American students be successful in these courses when compared to other
schools that do not have targeted programs. Therefore, interpreting the relationships with
the number of advanced placement courses taken must be done cautiously.
Recommendations for Further Research
This study could be replicated with a greater sample size. It would be interesting
to replicate this study across settings where the student population was more
homogeneous and compare the racial identity results for differences. It would improve
the generalizability of this study if it could be conducted with regional samples across the
United States to compare differences among students in similar size schools to explore
regional issues with regard to race. The understandings explored in this study could
benefit from qualitative research such as ethnographic studies such as interviews with
students to gain their viewpoints of the experience.
Further research could benefit by adding other variables to the model. This
research did not show significant relationships among the racial identity factors and
perceived school support; perhaps other variables such as student self-concept or
97
personality would show stronger relationships with racial identity and academic
achievement. Adding these variables to the model may yield important findings that
would help educators to improve the educational experience and outcomes for African
American students.
98
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APPENDIX A
LETTER OF INFORMED CONSENT
INFORMED CONSENT DOCUMENT
Project Title:
Pathways toward Progress: Understanding the roles of cognitive
ability, personality, motivation and racial identity in African American students
Principal Investigator:
Antonia Szymanski, ABD, University of Iowa
Research Team Contact:
Antonia Szymanski (319) 541-2653 email: [email protected]
If you are the parent/guardian of a child under 18 years old who is being invited to be
in this study, you will be asked to read and sign this document to give permission for
your child to participate.
If you are a teenager reading this document because you are being invited to be in this
study, the word ―you‖ in this document refers to you.
This consent form describes the research study to help you decide if you want to
participate. This form provides important information about what you will be asked to do
during the study, about the risks and benefits of the study, and about your rights as a
research subject.
If you have any questions about or do not understand something in this form, you
should ask the research team for more information.
You should discuss your participation with anyone you choose such as family or
friends.
Do not agree to participate in this study unless the research team has answered
your questions and you decide that you want to be part of this study.
WHAT IS THE PURPOSE OF THIS STUDY?
This is a research study. We are inviting you to participate in this research study because
you are an African American 11th or 12th grader in St. Paul Public School District.
The purpose of this research study is to understand how your ability to learn, motivation,
and feelings about being Black affect the GPA and course selection of African American
students.
HOW MANY PEOPLE WILL PARTICIPATE?
Approximately 600 people will take part in this study conducted by researchers from the
University of Iowa.
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HOW LONG WILL I BE IN THIS STUDY?
If you agree to take part in this study, your involvement will last for approximately one
hour.
WHAT WILL HAPPEN DURING THIS STUDY?
You will be given a short survey that asks about how you see yourself and your abilities.
The survey asks questions about your feelings about yourself and your feelings toward
your school environment. You may skip any questions that you do not wish to answer.
The survey takes about one hour to complete.
We will meet in a room at school to complete the survey.
We will obtain your test scores for the standardized tests you took in 6th grade, your
current GPA, and the total number of AP or honors classes you have taken. The school
will provide this information to the researcher after receiving this consent form.
WHAT ARE THE RISKS OF THIS STUDY?
You may experience one or more of the risks indicated below from being in this study. In
addition to these, there may be other unknown risks, or risks that we did not anticipate,
associated with being in this study.
It is possible that you may feel uncomfortable answering questions about yourself or your
classroom experiences. You may skip any questions that you do not wish to answer and
stop participating at any time.
You may be concerned that your decision whether or not to be in the study or your
responses on the survey may affect your class grades or your standing in school. We will
not reveal your answers to the teachers or anyone in the school.
WHAT ARE THE BENEFITS OF THIS STUDY?
You will not benefit directly from being in this study. However, we hope that, in the
future, other people might benefit from this study because we will provide new
information about African American learners.
WILL IT COST ME ANYTHING TO BE IN THIS STUDY?
You will not have any costs for being in this research study.
WILL I BE PAID FOR PARTICIPATING?
Enclosed in the envelope for the return of your survey will be a ticket. Separate the ticket
from the survey and write your name on the ticket. Place the ticket in the collection box
at the front of the classroom. You will be entered into a drawing for a $45 Visa gift card.
The winner will be drawn at the conclusion of the survey administration and the card will
be delivered that day.
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WHO IS FUNDING THIS STUDY?
The University of Iowa and the research team are receiving no payments from other
agencies, organizations, or companies to conduct this research study.
WHAT ABOUT CONFIDENTIALITY?
We will keep your participation in this research study confidential to the extent
permitted by law. However, it is possible that other people such as those indicated
below may become aware of your participation in this study and may inspect and
copy records pertaining to this research. Some of these records could contain
information that personally identifies you.
federal government regulatory agencies,
auditing departments of the University of Iowa, and
the University of Iowa Institutional Review Board (a committee that reviews and
approves research studies)
To help protect your confidentiality, we will use a number and not your name to identify
your study information. Your survey form will have a number and will be returned in an
envelope so that no one can see your answers. There will be a document that links your
name with the survey number. This list and this consent document will be stored so that
only the researcher can see them. We will destroy the list linking the number to your
name once your data has been entered into the computer. We will store all study materials
and information in locked files and password protected computer files. If we write a
report or article about this study or share the study data set with others, we will do so in
such a way that you cannot be directly identified.
IS BEING IN THIS STUDY VOLUNTARY?
Taking part in this research study is completely voluntary. You may choose not to take
part at all. If you decide to be in this study, you may stop participating at any time. If
you decide not to be in this study, or if you stop participating at any time, you won’t be
penalized or lose any benefits for which you otherwise qualify. Your decision whether or
not to be in this study will not affect the grades you receive in your courses.
WHAT IF I HAVE QUESTIONS?
We encourage you to ask questions. If you have any questions about the research study
itself, please contact: Antonia Szymanski (319) 541-2653. If you experience a researchrelated injury, please contact Dr. David Lohman (319) 335-5574.
If you have questions, concerns, or complaints about your rights as a research subject or
about research related injury, please contact the Human Subjects Office, 105 Hardin
Library, 600 Newton Road, The University of Iowa, Iowa City, Iowa, 52242, (319) 335-
111
6564, or e-mail [email protected]. General information about being a research subject can
be found by clicking ―Info for Public‖ on the Human Subjects Office web site,
http://research.uiowa.edu/hso. To offer input about your experiences as a research subject
or to speak to someone other than the research staff, call the Human Subjects Office at
the number above.
This Informed Consent Document is not a contract. It is a written explanation of what
will happen during the study if you decide to participate. You are not waiving any legal
rights by signing this Informed Consent Document. Your signature indicates that this
research study has been explained to you, that your questions have been answered, and
that you agree to take part in this study. You will receive a copy of this form.
Subject's Name (printed):
__________________________________________________________
Birthdate (MM/DD/YY)_______________ Student ID # (CIF)___________________
School______________
Do not sign this form if today’s date is on or after $STAMP_EXP_DT .
________________________________________________________________________
(Signature of Subject)
(Date)
Parent/Guardian’s Name and Relationship to Subject:
________________________________________________________________________
(Name - printed)
printed)
(Relationship to Subject -
Do not sign this form if today’s date is on or after $STAMP_EXP_DT .
________________________________________________________________________
(Signature of Parent/Guardian)
(Date)
112
APPENDIX B
DEMOGRAPHIC QUESTIONNAIRE
42 How do you identify yourself?
□
□
□
□
African
African American
Puerto Rican
Hispanic
□
Other:_____________________________
43
What is your father’s highest level of
education completed?
□
□
□
□
□
Some High School
High School Graduate
Some College
College Graduate
Don’t Know
113
44
What is your mother’s highest level of
education completed?
□
□
□
□
□
Some High School
High School Graduate
Some College
College Graduate
Don’t Know
Have you ever been identified to
45 participate in programs for gifted or
talented students?
If “yes”, tell how many years you
participated in programs for gifted or
talented students.
Yes
□
No
□
______________________
114
I am encouraged to work with students of
1 other races and cultures in school
activities.
I am encouraged to learn about different
2
races and cultures.
My teachers show me that they think it is
important for students of different races
3
and cultures at my school to get along with
each other.
Students from different races and cultures
4 are represented in important school
activities.
Very Rarely
Rarely
Sometimes
Often
How often do you have each of the
following experiences at your
school?
Very Often
APPENDIX C
PERCEIVED SCHOOL SUPPORT QUESTIONNAIRE
□ □ □ □ □
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□ □ □ □ □
□ □ □ □ □
5
Teachers take a personal interest in students.
6
Teachers go out of their way to help students.
7
In classes, students find it hard to get along
with each other.
8
If students want to talk to teachers about
something, teachers will find time to do it.
9
Students really enjoy their classes.
10
Teachers ask students what they want to
learn.
11
Teachers help students to organize their
work.
12
Teachers help students catch up when they
return from an absence.
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□□□
□□□
Strongly Disagree
Disagree
Neither Agree or
Disagree
Agree
How much do you agree with each of
the following statements about your
school?
Strongly Agree
115
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116
14
I feel that Blacks have made major
accomplishments and advancements.
15 I feel good about Black people.
16 Society views Black people as an asset.
17
In general, being Black is an important part
of my self-image.
18
Overall Blacks are considered good by
others.
19
Being Black is unimportant to my sense of
what kind of person I am.
20 I often regret that I am Black.
21 I am proud to be Black.
22
Most people consider Blacks, on average, to
be less effective than other racial groups.
23
Blacks are not respected by the broader
society.
24
In general, other groups view Blacks in a
positive manner.
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□□
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□□
□□
□□
□□
□□
Strongly
Disagree
Disagree
Overall, being Black has very little to do with
how I feel about myself.
Neither Agree
or Disagree
13
Agree
How much do you agree with each of
the following statements about how
you feel being Black?
Strongly Agree
APPENDIX D
MULTIDIMENSIONAL INVENTORY OF BLACK IDENTITY
117
25 In general, others respect Black people.
26 I am happy that I am Black.
27
I feel that the Black community has made
valuable contributions to this society.
28
Being Black is an important reflection of who
I am.
29
Being Black is not a major factor in my social
relationships.
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118
31
I feel good when I improve in my studies.
32
School is fun for me.
33
I like to learn more about interesting subjects.
34
I really like going to school.
35
High school helps me to feel good about
working towards excellence in my studies.
36
I feel good while reading about various
interesting subjects.
37
I enjoy discussions with my teachers.
38
I feel good when I know that I am doing
something better than before.
39
I feel satisfied when I succeed at difficult
academic tasks.
40
My classes help me to learn about things that
interest me.
41
It feels good to learn about new things.
Disagree
I enjoy learning new things.
Agree
Neither Agree or
Disagree
30
Strongly Agree
How much do you agree with each of
the following statements about why
you go to school?
Strongly Disagree
APPENDIX E
ACADEMIC MOTIVATION QUESTIONNAIRE
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119
APPENDIX F
TABLES
Table F1. Summary Statistics of Sample Recruitment District Wide
Total
Number of Consent Forms Sent Out and Distributed
by the Schools (Estimated)
Number of Completed Consent Forms Returned
271-300
84
Number of Surveys Completed
84
Response Rate
28 – 31%
Number of Participants with 6th Grade Tests Scores
Available
Number of Surveys Deleted from Analyses
56
28
Final Sample Size
56
Response Rate
19-21%
120
Table F2: Student Demographic Data (N = 56)
School A
School B
#
#
No Response
2
1
African
1
0
African American
31
12
Other – Mixed Race
8
1
Female
29
7
Male
13
7
11th
26
2
12th
16
12
Unknown
10
1
Some High School
3
3
High School Graduate
7
3
Some College
11
3
College Graduate
11
4
Unknown
1
1
Some High School
5
2
High School Graduate
7
6
Some College
16
4
College Graduate
13
1
Race By Which the Students Identified:
Sex:
Grade:
Father’s Education
Mother’s Education
121
Table F3. Chi-Squared Analysis of Demographic Data
Race
Χ2
1.564
P value
.668
Effect Size
.167
Sex
1.659
.198
.172
Grade
9.524
.002
.412
Father’s Education
3.736
.443
.25
Mother’s Education
6.464
.167
.339
Variable
122
Table F4. Summary Statistics of Scores on Outcome and Control Measures
Mean
Standard
Deviation
Mean Difference
t
Significance
6th Grade
Achievement Test
Scores
School
School
A
B
1326
1294
60
54
32.05
1.78
.082
Number of AP
Classes Taken
School
A
4.39
3.48
Grade Point
Average
School
B
3.39
3.79
School
A
2.08
.695
.994
.905
.370
School
B
1.75
.58
.324
1.57
.122
Table F5. Summary Statistics of Scores on Self-Report Measures
Centrality
(CEN)
Private
Regard
(PRI)
Public
Regard
(PUB)
6
Perceived
School
Support
(SUP)
12
Academic
Intrinsic
Motivation
(MOT)
12
Number of Items
5
6
Minimum Possible
5
6
6
12
12
Maximum Possible
25
30
30
60
60
Mean
15.40
26.25
16.63
42.26
50.35
SD
3.78
3.40
3.61
7.27
4.52
6
13
8
26
41
Minimum
Observed
Maximum
Observed
α
23
31
24
75
60
.642
.740
.658
.748
.783
α*
.75
.68
.74
.72
.83
Note: α* denotes the Cronbach Alpha reliability estimates obtained in previous research studies cited in Chapter
2.
123
Table F6. Correlations between Scores on Self-Report Measures
Pearson Product Moment (Zero-Order) Correlations
Centrality
(CEN)
Private
Regard
(PRI)
Public
Regard
(PUB)
Perceived
Support
(SUP)
Academic
Intrinsic
Motivation
(MOT)
Centrality
Private
Regard
Public Regard
Perceived
Support
Academic
Intrinsic
Motivation
.316
(p = .018)
.084
(p = .538)
.269
(p = .045)
-.178
(p = .189)
.070
(p = .608)
.220
(p = .103)
-.089
(p = .514)
.015
(p = .911)
-.091
(p= .505)
.296
(p = .027)
Disattenuated Correlations
Centrality
Private
Regard
Public
Regard
Perceived
Support
Academic
Intrinsic
Motivation
.386
1
Centrality
Private
Regard
Public Regard
Perceived
Support
Academic
Intrinsic
Motivation
.458
.129
.385
-.257
.094
.313
-.125
.019
-.126
124
Table F7. Regression Model Summary for Relationships among Perceived Support
(SUP), Racial Identity Factors (CEN, PRI, PUB), and Academic Intrinsic Motivation
(MOT)
Change Statistics
Std
Model
R
R2
Adj
R2
Error
Est
R2
Sig F
F
df1
Change
Change
df2
Change
3.1
.296a
.088
.071
4.31
.088
5.197
1
54
.027
3.2
.340b
.116
.046
4.37
.028
.536
3
51
.660
3.3
.440c
.194
.076
4.30
.078
1.546
3
48
.215
a. Predictors: (Constant), Perceived Support
b. Predictors: (Constant), Perceived Support, Centrality, Private Regard, Public Regard
c. Predictors: (Constant), Perceived Support, Centrality, Private Regard, Public Regard, Centrality * Perceived
Support, Private Regard * Perceived Support, Public Regard * Perceived Support
125
Table F8. Regression and Correlation Coefficients for Relationships among Perceived
Support (SUP), Racial Identity Factors (CENT, PRI, PUB), and Academic Intrinsic
Motivation (MOT)
Unstandardized
Coefficients
Model
3.1
3.2
3.3
Stdzd
Coeff
Correlations
t
β
Std
Error
Const
42.782
3.419
SUP
.181
.080
Const
44.46
5.824
SUP
.199
.085
CEN
-.038
PRI
Sig
B
ZeroOrder
Partial
Part
.296
.296
.296
12.514
.000
2.280
.027
7.633
.000
.325
2.352
.023
.296
.313
.310
.168
-.032
-.227
.821
-.089
-.032
-.030
.064
.188
.049
.342
.734
.015
.048
.045
PUB
-.212
.172
-.173
-1.236
.222
-.091
-.170
-.163
Const
85.327
34.687
2.460
.018
SUP
-.729
.825
-1.191
-.884
.381
.296
-.127
-.115
CEN
.428
1.270
.363
.337
.737
-.089
.049
.044
PRI
-2.434
1.209
-1.856
-2.005
.051
.015
-.278
-.260
PUB
.858
1.254
.698
.684
.497
-.091
.098
.089
SUP*CEN
-.010
.030
-.379
-.326
.746
.099
-.047
-.042
SUP*PRI
.057
.027
3.198
2.094
.042
.259
.289
.271
SUP*PUB
-.026
.030
-1.216
-.847
.401
.099
-.121
-.110
.296
Dependent Variable: Academic Intrinsic Motivation (MOT)
126
Table F9. Regression Model Summary for Relationships among Perceived Support
(SUP), Racial Identity Factors (CEN, PRI, PUB), and Academic Intrinsic Motivation
(MOT) – Reduced Models
R
Change
.088
Change Statistics
F
df1
df2
Change
5.197
1
54
Sig F
Change
.027
3.1
.296 a
.088
.071
Std
Error
Est
4.31
3.2A
.299 b
.089
.055
4.35
.001
.079
1
53
.779
3.4A
.300 c
.090
.038
4.39
.001
.063
1
52
.803
Model
R
R2
Adj
R2
2
a. Predictors: (Constant), Perceived Support
b. Predictors: (Constant), Perceived Support, Centrality
c. Predictors: (Constant), Perceived Support, Centrality, Centrality * Perceived Support
Dependent Variable: Academic Intrinsic Motivation
R
Change
.088
Change Statistics
F
df1
df2
Change
5.197
1
54
Sig F
Change
.027
3.1
.296a
.088
.071
Std
Error
Est
4.31
3.2B
.296 b
.088
.053
4.35
.000
.002
1
53
.967
3.4B
.397 c
.158
.109
4.22
.070
4.329
1
52
.042
Model
R
R2
Adj
R2
2
a. Predictors: (Constant), Perceived Support
b. Predictors: (Constant), Perceived Support, Private Regard
c. Predictors: (Constant), Perceived Support, Private Regard, Private Regard * Perceived Support
Dependent Variable: Academic Intrinsic Motivation
127
Table F9 Continued
R
Change
.088
Change Statistics
F
df1
df2
Change
5.197
1
54
Sig F
Change
.027
3.1
.296 a
.088
.071
Std
Error
Est
4.31
3.2C
.337b
.113
.080
4.29
.026
1.530
1
53
.222
3.4C
..340 c
.116
.065
4.33
.002
.147
1
52
.703
Model
R
R2
Adj
R2
2
a. Predictors: (Constant), Perceived Support
b. Predictors: (Constant), Perceived Support, Public Regard
c. Predictors: (Constant), Perceived Support, Public Regard, Public Regard * Perceived Support
Dependent Variable: Academic Intrinsic Motivation
128
Table F10. Regression and Correlation Coefficients for Relationships among Perceived
Support (SUP), Racial Identity Factors (CENT, PRI, PUB), and Academic Intrinsic
Motivation (MOT) – Reduced Models (A, B, and C)
Unstandardized
Coefficients
Stdzd
Coeff
Model
3.1
3.2A
3.3A
Correlations
t
β
Std
Error
Const
42.782
3.419
SUP
.181
.080
Const
43.635
4.588
SUP
.177
.082
CEN
-.044
.157
Const
47.927
17.693
SUP
.073
.422
CEN
-.350
SUP*CEN
.007
Sig
B
ZeroOrder
Partial
Part
.296
.296
.296
12.514
.000
2.280
.027
9.510
.000
.290
2.174
.034
.296
.286
.285
-.038
-.282
.779
-.089
-.039
-.037
2.709
.009
.120
.174
.863
.296
.024
.023
1.226
-.297
-.285
.777
-.089
-.040
-.038
.030
.286
.251
.803
.099
.035
.033
.296
Dependent Variable: Academic Intrinsic Motivation (MOT)
129
Table F10 Continued
Unstandardized
Coefficients
Stdzd
Coeff
t
Sig
Correlations
Model
3.1
3.2B
3.3B
β
Std
Error
Const
42.782
3.419
SUP
.181
.080
Const
42.958
5.488
SUP
.182
.081
PRI
-.007
.172
Const
100.961
28.382
SUP
-1.162
.651
PRI
-2.225
SUP*PRI
.051
B
ZeroOrder
Partia
l
Part
.296
.296
.296
12.51
.000
2.28
.027
7.83
.000
.297
2.26
.028
.296
.296
.296
-.005
-.04
.967
.015
-.006
-.005
3.56
.001
-1.898
-1.79
.080
.296
-.240
-.227
1.079
-1.703
-2.06
.044
.015
-.275
-.262
.025
2.880
2.08
.042
.259
.277
.265
.296
Dependent Variable: Academic Intrinsic Motivation (MOT)
130
Table F10 Continued
Unstandardized
Coefficients
Stdzd
Coeff
Model
3.1
3.2C
3.3C
Correlations
t
β
Std
Error
Const
42.782
3.419
SUP
.181
.080
Const
45.194
3.921
SUP
.204
.081
PUB
-.201
.163
Const
37.711
19.942
SUP
.388
.489
PUB
.259
SUP*PUB
-.011
Sig
B
12.52
.296
ZeroOrder
Partial
Part
.296
.296
.296
.000
2.280 .027
11.53
.000
.332
2.51
.015
.296
.326
.324
-.164
-1.24
.222
-.091
-.167
-.160
1.89
.064
.634
.79
.431
.296
.109
.103
1.213
.211
.21
.832
-.091
.030
.028
.029
-.532
-.38
.703
.099
-.053
-.050
Dependent Variable: Academic Intrinsic Motivation (MOT)
131
Table F11. Regression Model Summary for Relationships among Sixth Grade Academic
Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity Factors
(CEN, PRI, PUB), and Grade Point Average (GPA)
Change Statistics
F
df1
df2
Change
27.891
1
54
.328
Std
Error
Est
.555
R
Change
.341
.342
.317
.560
.001
.117
1
53
.734
.627c
.393
.333
.553
.051
1.408
3
50
.251
.631d
.398
.296
.568
.005
.135
3
47
.939
Model
R
R2
Adj
R2
4.1
.584a
.341
4.2
.585b
4.3
4.4
2
Sig F
Change
.000
a. Predictors: (Constant), 6th Grade Academic Achievement
b. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation
c. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Centrality, Private
Regard, Public Regard
d. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Centrality, Private
Regard, Public Regard, Centrality *Academic Motivation, Private Regard *Academic Motivation, Public
Regard * Academic Motivation
Dependent Variable: Grade Point Average (GPA)
132
Table F12. Regression and Correlation Coefficients for Relationships among Sixth Grade
Academic Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity
Factors (CEN, PRI, PUB), and Grade Point Average (GPA)
Unstandardized
Coefficients
Model
Const
-6.731
Std
Error
1.654
6TH
.007
.001
Const
-6.575
1.829
6TH
.007
.001
MOT
-.006
.017
Const
-5.778
1.842
6TH
.006
.001
MOT
-.005
CEN
β
4.1
4.2
4.3
4.4
Stdzd
Coeff
Β
.584
Correlations
t
Sig
ZeroOrder
Partial
Part
.584
.584
.584
-4.070
.000
5.281
.000
-3.540
.001
.586
5.249
.000
.584
.585
.585
-.038
-.342
.734
-.003
-.047
-.038
-3.137
.003
.544
4.531
.000
.584
.540
.499
.017
-.033
-.297
.768
-.003
-.042
-.033
.033
.022
.183
1.499
.140
.352
.207
.165
PRI
-.005
.024
-.024
-.198
.844
.143
-.028
-.022
PUB
-.029
.021
-.157
-1.363
.179
-.094
-.189
-.150
Const
-10.074
8.644
-1.165
.250
6TH
.006
.001
.552
4.388
.000
.584
.539
.496
MOT
.077
.164
.511
.473
.639
-.003
.069
.053
CEN
.131
.296
.733
.441
.661
.352
.064
.050
PRI
.025
.307
.129
.083
.934
.143
.012
.009
PUB
.079
.257
.423
.306
.761
-.094
.045
.035
MOT*CEN
-.002
.006
-.586
-.342
.734
.343
-.050
-.039
MOT*PRI
-.001
.006
-.170
-.091
.928
.108
-.013
-.010
MOT*PUB
-.002
.005
-.619
-.423
.674
-.097
-.062
-.048
Dependent Variable: Grade Point Average (GPA)
133
Table F13. Regression Model Summary for Relationships among Sixth Grade Academic
Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity Factors
(CEN, PRI, PUB), and Number of Advanced placement Courses (NAP)
Change Statistics
F
df1
df2
Change
13.701
1
54
.188
Std
Error
Est
3.203
R
Change
.202
.210
.181
3.217
.008
.539
1
53
.466
.503c
.253
.178
3.222
.042
.944
3
50
.427
.662d
.439
.343
2.880
.186
5.185
3
47
.004
Model
R
R2
Adj
R2
5.1
.450a
.202
5.2
.459b
5.3
5.4
2
Sig F
Change
.001
a. Predictors: (Constant), 6th Grade Academic Achievement
b. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation
c. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Centrality, Private
Regard, Public Regard
d. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Centrality, Private
Regard, Public Regard, Centrality *Academic Motivation, Private Regard *Academic Motivation, Public
Regard * Academic Motivation
Dependent Variable: Number of Advanced placement Courses Taken
134
Table F14. Regression and Correlation Coefficients for Relationships among Sixth Grade
Academic Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity
Factors (CENT, PRI, PUB), and Number of Advanced placement Courses Taken (NAP)
Unstandardized
Coefficients
Model
5.1
5.2
5.3
5.4
β
Std
Error
Const
-31.122
9.536
6TH
.027
.007
Const
-34.291
10.51
6TH
.026
.007
MOT
.071
.097
Const
-32.787
10.72
6TH
.029
.008
MOT
.074
CEN
Stdzd
Coeff
Correlations
t
Sig
Β
ZeroOrder
Partial
Part
.450
.450
.450
-3.264
.002
3.701
.001
-3.264
.002
.444
3.634
.001
.450
.447
.444
.090
.734
.466
.117
.100
.090
-3.059
.004
.493
3.702
.001
.450
.464
.453
.098
.093
.752
.456
.117
.106
.092
.034
.127
.036
.265
.792
.128
.037
.032
PRI
-.228
.141
-.220
-1.619
.112
-.069
-.223
-.198
PUB
-.001
.124
.001
-.009
.993
-.020
-.001
-.001
Const
-197.53
43.79
-4.511
.000
6TH
.032
.007
.539
4.435
.000
.450
.543
.485
MOT
3.236
.828
4.077
3.907
.000
.117
.495
.427
CEN
3.42
1.501
3.654
2.278
.027
.128
.315
.249
PRI
3.178
1.553
3.065
2.046
.046
-.069
.286
.224
PUB
1.087
1.302
1.114
.835
.408
-.020
.121
.091
MOT*CEN
-.068
.029
-3.827
-2.313
.025
.150
-.320
-.253
MOT*PRI
-.066
.030
-3.971
-2.186
.034
-.018
-.304
-.239
MOT*PUB
-.022
.026
-1.185
-.838
.406
.006
-.121
-.092
.450
Dependent Variable: Number of Advanced placement Courses Taken (NAP)
135
Table F15. Regression Model Summary for Relationships among Sixth Grade Academic
Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity Factors
(CEN, PRI, PUB), and Number of Advanced Placement Courses (NAP) Reduced Models
(A, B, and C)
Change Statistics
F
df1
df2
Change
13.701
1
54
.188
Std
Error
Est
3.203
R
Change
.202
.210
.181
3.217
.008
.539
1
53
.466
.459 c
.211
.165
3.247
.000
.023
1
52
.881
. 532d
.283
.227
3.125
.072
5.127
1
51
.028
Model
R
R2
Adj
R2
5.1
.450a
.202
5.2
.459b
5.3A
5.5A
2
Sig F
Change
.001
a. Predictors: (Constant), 6th Grade Academic Achievement
b. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation
c. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Centrality
d. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Centrality,
Centrality *Academic Motivation
Dependent Variable: Number of Advanced placement Courses Taken
Std
Error
Est
Change Statistics
F
df1
df2
Change
13.701
1
54
Model
R
R
Adj
R2
5.1
.450a
.202
.188
3.203
R
Change
.202
5.2
.459b
.210
.181
3.217
.008
.539
1
53
.466
5.3B
. 502c
.252
.208
3.161
.041
2.867
1
52
.096
5.5B
.601 d
.361
.311
2.950
.109
8.701
1
51
.005
2
2
Sig F
Change
.001
a. Predictors: (Constant), 6th Grade Academic Achievement
b. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation
c. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Private Regard
d. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Private Regard,
Private Regard *Academic Motivation
Dependent Variable: Number of Advanced placement Courses Taken
136
Table F15 Continued
Change Statistics
F
df1
df2
Change
13.701
1
54
.188
Std
Error
Est
3.203
R
Change
.202
.210
.181
3.217
.008
.539
1
53
.466
.462 c
.213
.168
3.241
.003
.189
1
52
.665
.527 d
.277
.221
3.137
.064
4.530
1
51
.038
Model
R
R2
Adj
R2
5.1
.450a
.202
5.2
.459b
5.3C
5.5C
2
Sig F
Change
.001
a. Predictors: (Constant), 6th Grade Academic Achievement
b. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation
c. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Public Regard
d. Predictors: (Constant), 6th Grade Academic Achievement, Academic Intrinsic Motivation, Public Regard,
Public Regard *Academic Motivation
Dependent Variable: Number of Advanced placement Courses Taken
137
Table F16. Regression and Correlation Coefficients for Relationships among Sixth Grade
Academic Achievement (6TH), Academic Intrinsic Motivation (MOT), Racial Identity
Factors (CENT, PRI, PUB), and Number of Advanced placement Courses Taken (NAP)
Reduced Models (A, B, and C)
Unstandardized
Coefficients
Model
5.1
5.2
5.3A
5.5A
Stdzd
Coeff
Correlations
t
β
Std
Error
Const
-31.122
9.54
6TH
.027
.007
Const
-34.291
10.51
6TH
.026
.007
.444
3.702
MOT
.071
.097
.090
.734
Const
-34.463
10.66
6TH
.027
.008
.451
3.424
MOT
.070
.099
.088
CEN
-.019
.124
-.020
Const
-93.092
27.85
6TH
.029
.008
.496
3.860
MOT
1.154
.488
1.454
CEN
3.477
1.548
MOT*CEN
-.069
.030
Sig
ZeroOrder
Partial
Part
.450
.450
.450
.001
.450
.447
.444
.466
.117
.100
.090
.000
.450
.429
.422
.705
.484
.117
.097
.087
-.150
.881
.128
-.021
-.019
.000
.450
.475
.458
2.363
.022
.117
.314
.280
3.715
2.246
.029
.128
.300
.266
-3.886
-2.264 .028
.150
-.302
-.269
Β
-3.264 .002
.450
3.701
.001
-3.059 .002
-3.231 .002
-3.342 .002
Dependent Variable: Number of Advanced placement Courses Taken (NAP)
138
Table F16 Continued
Unstandardized
Coefficients
Model
5.1
5.2
5.3B
5.5B
Stdzd
Coeff
Correlations
t
β
Std
Error
Const
-31.122
9.54
6TH
.027
.007
Const
-34.291
10.51
6TH
.026
.007
.444
3.702
MOT
.071
.097
.090
.734
Const
-33.157
10.35
6TH
.030
.007
.504
4.023
MOT
.071
.095
.089
.744
PRI
-.219
.130
-.212
Const
-143.32
38.576
6TH
.028
.007
.477
4.074
MOT
2.236
.739
2.817
PRI
4.059
1.455
MOT*PUB
-.083
.028
Sig
ZeroOrder
Partial
Part
.450
.450
.450
.001
.450
.447
.444
.466
.117
.100
.090
.000
.450
.487
.483
.460
.117
.103
.089
-1.693 .096
-.069
-.229
-.203
.000
.450
.495
.456
3.024
.004
.117
.390
.339
3.914
2.789
.007
-.069
.364
.312
-4.99
-2.950 .005
-.018
-.382
-.330
Β
-3.264 .002
.450
3.701
.001
-3.059 .002
-3.205 .002
-3.715 .001
Dependent Variable: Number of Advanced placement Courses Taken (NAP)
139
Table F16 Continued
Unstandardized
Coefficients
Model
5.1
5.2
5.3C
5.5C
Stdzd
Coeff
Correlations
t
β
Std
Error
Const
-31.122
9.54
6TH
.027
.007
Const
-34.291
10.51
6TH
.026
.007
.444
3.634
MOT
.071
.097
.090
.734
Const
Sig
ZeroOrder
Partial
Part
.450
.450
.450
.001
.450
.447
.444
.466
.117
.100
.090
Β
-3.264 .002
.450
3.701
.001
-3.264 .002
-33.626 10.695
-3.144 .003
6TH
.027
.007
.450
3.632
.001
.450
.450
.447
MOT
.067
.098
.085
.683
.498
.117
.094
.084
PUB
-.053
.121
-.054
-.435
.665
-.020
-.060
-.054
Const
-80.052 24.143
-3.316 .002
6TH
.027
.007
.452
3.769
.000
.450
.467
.449
MOT
.990
.444
1.248
2.230
.030
.117
.298
.265
PUB
2.650
1.275
2.717
2.078
.043
-.020
.279
.247
MOT*PUB
-.054
.025
-2.917 -2.128 .038
.006
-.286
-.253
Dependent Variable: Number of Advanced placement Courses Taken (NAP)
140
Table F17. Regression Model Summary for Relationships among Sixth Grade Academic
Achievement (6TH), Perceived School Support (SUP), Racial Identity Factors (CEN,
PRI, PUB), and Grade Point Average (GPA)
Change Statistics
F
df1
df2
Change
27.891
1
54
.328
Std
Error
Est
.555
R
Change
.341
.357
.333
.554
.016
1.336
1
53
.253
.658c
.433
.377
.535
.077
2.255
3
50
.093
.681d
.464
.372
.537
.030
.880
3
47
.458
Model
R
R2
Adj
R2
6.1
.584a
.341
6.2
.597b
6.3
6.4
2
Sig F
Change
.000
a. Predictors: (Constant), 6th Grade Academic Achievement
b. Predictors: (Constant), 6th Grade Academic Achievement, Perceived School Support
c. Predictors: (Constant), 6th Grade Academic Achievement, Perceived School Support, Centrality, Private
Regard, Public Regard
d. Predictors: (Constant), 6th Grade Academic Achievement, Perceived School Support, Centrality, Private
Regard, Public Regard, Centrality *Academic Motivation, Private Regard *Academic Motivation, Public
Regard * Academic Motivation
Dependent Variable: Grade Point Average
141
Table F18. Regression and Correlation Coefficients for Relationships among Sixth Grade
Academic Achievement (6TH), Perceived School Support (SUP), Racial Identity Factors
(CENT, PRI, PUB), and Grade Point Average (GPA)
Unstandardized
Coefficients
Model
6.1
6.2
6.3
6.4
β
Std
Error
Const
-6.731
1.654
6TH
.007
.001
Const
-7.351
1.734
6TH
.007
.001
SUP
.012
.010
Const
-6.825
1.691
6TH
.006
.001
SUP
.020
CEN
Stdzd
Coeff
Correlations
t
Sig
Β
ZeroOrder
Partial
Part
.584
.584
.584
-4.070
.000
5.281
.000
-4.240
.000
.592
5.360
.000
.584
.593
.590
.128
1.156
.253
.091
.157
.127
-4.036
.000
.548
4.742
.000
.584
.557
.505
.010
.213
1.908
.062
.091
.260
.203
.042
.021
.233
1.949
.057
.352
.266
.207
PRI
-.009
.023
-.045
-.380
.706
.143
-.054
-.040
PUB
-.037
.021
-.200
-1.764
.084
-.094
-.242
-.188
Const
-7.971
4.685
-1.701
.095
6TH
.006
.001
.491
3.924
.000
.584
.497
.419
SUP
.065
.103
.702
.632
.530
.091
.092
.068
CEN
-.113
.159
-.632
-.712
.480
.352
-.103
-.076
PRI
.031
.153
.159
.206
.838
.143
.030
.022
PUB
.157
.165
.843
.948
.348
-.094
.137
.101
SUP*CEN
.004
.004
.932
.968
.338
.395
.140
.103
SUP*PRI
-.001
.003
-.279
-.217
.829
.152
-.032
-.023
SUP*PUB
-.005
.004
-1.483
-1.187
.241
-.045
-.171
-.127
Dependent Variable: Grade Point Average (GPA)
.584
142
Table F19. Regression Model Summary for Relationships among Sixth Grade Academic
Achievement (6TH), Perceived School Support (SUP), Racial Identity Factors (CEN,
PRI, PUB), and Number of Advanced Placement Courses Taken (NAP)
Change Statistics
F
df1
df2
Change
13.701
1
54
.188
Std
Error
Est
3.203
R
Change
.202
.235
.206
3.167
.032
2.238
1
53
.141
.539c
.290
.219
3.140
.056
1.307
3
50
.282
.579d
.336
.223
3.133
.045
1.071
3
47
.371
Model
R
R2
Adj
R2
7.1
.450a
.202
7.2
.484b
7.3
7.4
2
Sig F
Change
.001
a. Predictors: (Constant), 6th Grade Academic Achievement
b. Predictors: (Constant), 6th Grade Academic Achievement, Perceived School Support
c. Predictors: (Constant), 6th Grade Academic Achievement, Perceived School Support, Centrality, Private
Regard, Public Regard
d. Predictors: (Constant), 6th Grade Academic Achievement, Perceived School Support, Centrality, Private
Regard, Public Regard, Centrality *Academic Motivation, Private Regard *Academic Motivation, Public
Regard * Academic Motivation
Dependent variable: Number of Advanced placement Courses Taken
143
Table F20. Regression and Correlation Coefficients for Relationships among Sixth Grade
Academic Achievement (6TH), Perceived School Support (SUP), Racial Identity Factors
(CENT, PRI, PUB), and Number of Advanced placement Courses Taken (NAP)
Unstandardized
Coefficients
Model
7.1
7.2
7.3
7.4
β
Std
Error
Const
-31.122
9.536
6TH
.027
.007
Const
-35.713
9.915
6TH
.027
.007
SUP
.088
.059
Const
-34.197
9.922
6TH
.030
.008
SUP
.110
CEN
Stdzd
Coeff
Correlations
t
Sig
Β
ZeroOrder
Partial
Part
.450
.450
.450
-3.264
.002
3.701
.001
-3.602
.001
.461
3.830
.000
.450
.466
.460
.180
1.496
.141
.151
.201
.180
-3.446
.001
.511
3.946
.000
.450
.487
.470
.061
.226
1.802
.078
.151
.247
.215
.067
.125
.071
.534
.596
.128
.075
.064
PRI
-.243
.137
-.234
-1.768
.083
-.069
-.243
-.211
PUB
-.058
.124
-.060
-.473
.638
-.020
-.067
-.056
Const
-77.321
-2.829
.007
6TH
.031
.008
.520
3.736
.001
.450
.479
.444
SUP
1.137
.601
2.338
1.892
.065
.151
.266
.225
CEN
1.023
.926
1.093
1.105
.275
.128
.159
.131
PRI
.418
.893
.403
.468
.642
-.069
.068
.056
PUB
.667
.964
.684
.692
.493
-.020
.100
.082
SUP*CEN
-.024
.022
-1.168
-1.090
.281
.199
-.157
-.130
SUP*PRI
-.015
.020
-1.076
-.752
.456
.064
-.109
-.089
SUP*PUB
-.018
.023
-1.065
-.766
.448
.046
-.111
-.091
.450
27.33
5
Dependent Variable: Number of Advanced placement Courses Taken (NAP)
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