THE IMPACT OF TEACHERS’ ATTITUDES AND PERCEPTIONS ON THE UNDER
REPRESENTATION OF AFRICAN AMERICAN STUDENTS IN
GIFTED EDUCATION PROGRAMS
A Thesis by
J. Kathryn Schreiner
B. S., University of Missouri, Columbia, 2010
Submitted to the Department of Counseling, Educational and School Psychology
and the faculty of the Graduate School of
Wichita State University
in partial fulfillment of
the requirements for the degree of
Master of Education
May 2010
© Copyright 2010 by J. Kathryn Schreiner
All Rights Reserved
ii
THE IMPACT OF TEACHERS’ ATTITUDES AND PERCEPTIONS ON THE UNDER
REPRESENTATION OF AFRICAN AMERICAN STUDENTS IN
GIFTED EDUCATION PROGRAMS
The following faculty members have examined the final copy of this Thesis for
form and content and recommend that it be accepted in partial fulfillment of the
requirements for the degree of Master of Education with a major in Educational
Psychology.
__________________________________________
Linda Bakken, Committee Chair
__________________________________________
Marlene Schommer-Aikens, Committee Member
__________________________________________
Gwendolyn Mukes, Committee Member
iii
ACKNOWLEDGEMENTS
I am deeply grateful to my husband and family who have shown me great love and
encouragement while I worked on this project. Thank you for all the time, energy and
patience you have shown while I worked to finish this degree. Thank you to Dr. Linda
Bakken, my thesis chair, for her incredible ability to encourage and lead, and most of all
for the knowledge and wisdom she brought to the entire process. Thank you also to Dr.
Marlene Schommer-Aikens for her vast knowledge of statistics and her amazing ability to
make it even somewhat understandable to the lowly grad student. Thanks to Dr.
Gwendolyn Mukes for getting the ball rolling on this research and for all your great ideas
and input. I had an amazing time working with each of you.
iv
ABSTRACT
This research studied the potential impact of teachers’ attitudes and perceptions on the
under representation of African American students in gifted education programs. The
study was conducted in an urban, Midwest school district with 322 elementary school
teachers participating. Results indicated that, in general, teachers perceive that African
American learners have a more difficult time learning than do students from other ethnic
groups. The mean score for White teachers was significantly higher than the mean scores
for Minorities. Results also indicated that, in general, teachers perceive that, although
African American learners are served at a higher rate than White students in special
education programs, they are not misidentified, and thus belong in those programs when
they are placed there. The mean scores of Minority teachers, teachers over the age of 35,
and teachers who had taught more than 15 years were significantly lower, although their
scores still indicated a general agreement with the idea that they are not misidentified.
The final significant finding was that younger teachers (22 – 35 years of age) felt that
they have had more adequate training in how to teach students from different cultural
backgrounds. This did not, however, seem to make any difference in their attitudes
toward African American learners’ ability to learn or misidentification.
v
TABLE OF CONTENTS
CHAPTER
PAGE
INTRODUCTION
The Problem
Definitions
Overview
1
3
4
LITERATURE REVIEW
Research on Causes
Theoretical Perspective
Teacher Attitudes and Behavior
Summary
Research Questions
5
8
12
15
18
METHODOLOGY
Sample
Instrument
Procedure
20
20
21
RESULTS
Psychometric Analyses
Analysis Addressing Research Questions
Descriptive Statistics
Multivariate Analysis of Variance
22
23
24
25
DISCUSSION
Theoretical Implications
Limitations
Recommendations for Future Research
30
32
33
REFERENCES
34
APPENDICES
A. Consent Form
B. Survey
C. Psychometric Analyses
39
40
43
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LIST OF TABLES
TABLE
PAGE
1. Numbers, Means and Standard Deviations of Each Factor for
Each Demographic Variable
25
2. MANOVA for Age and Misidentification, Ability to Learn,
and Adequate Training
26
3. MANOVA for Ethnicity and Misidentification, Ability to Learn,
and Adequate Training
27
4. MANOVA for Years Taught and Misidentification, Ability to Learn, 27
and Adequate Training
5. MANOVA for Gender and Misidentification, Ability to Learn,
and Adequate Training
vii
28
Chapter 1
The Problem
The under representation of African American students in gifted programs is a
very real issue in American schools. It is also an issue that is not openly discussed very
often. Educators and researchers may look at the achievement gap, which is a serious
problem in and of itself; however, the fact that African American students are over
represented in special education and under represented in gifted programs is a problem
that can no longer continue to be overlooked (Ford, Grantham & Whiting, 2008; Smith &
Kozleski, 2005; Suinn & Borrayo, 2008).
The problem of under representation of African Americans in gifted education
programs could have many roots. According to some researchers it may not be as
socially acceptable to be gifted in the Black community (Ford, Grantham & Whiting,
2008; Ogbu, 2004). It could be that current recommendation and testing practices are not
sufficient to identify giftedness in African Americans (Suinn & Borrayo, 2008; Williams,
2007). Or could it be that teachers themselves are not willing or able to see past their
own perceptions to see the qualities of giftedness in their African American students.
There has been considerable research over the years concerning how to help
minority students be more successful in school, or how to close the achievement gap
(Ford, Grantham, & Whiting, 2008; Suinn & Borrayo, 2008). There has also been
research on the over-representation of minority students recommended for, and placed, in
special education. Smith and Kozleski (2005; cited in Boone & King-Berry, 2007) found
that “African American students comprise 16% of school enrollment, but more than 21%
of total special education enrollment and more than 31% and 23% of students classified
1
as having mild mental retardation, and emotional disturbance, respectively” (p 7). Other
researchers (e.g., Oswald & Coutinho, 2006) found that Black students are almost one
and half times more likely than White students to be labeled with a disability (Boone &
King-Berry, 2007). Finally, to put this all in perspective, according to the US
Department of Education (2005; cited in Boone and King-Berry, 2007), “compared to
White peers, Black students are 3.04 times more likely to receive special education and
related services for mental retardation and 2.25 times more likely to receive special
education and related services for serious emotional disturbance than ALL other
racial/ethnic groups COMBINED” (Boone and King-Berry, 2007, 7).
Williams (2007) interviewed African American parents about the issue of over
representation in special education and found several interesting perceptions. Some
parents felt that African American students are unfairly put in special education because
teachers do not understand the culture of the students. Another participant in the study
said that psychologists who evaluate the students also do not understand the cultural
background of children of color, and thus are more likely to recommend placement in
special education. Another participant said that she felt placements were determined
unilaterally, where a teacher simply calls parents and tells them they need to sign some
forms, even though the parents may not really understand what they are signing. And
one final concern was that because teachers have such pressure for their students to
perform well on state-mandated examinations, they are more likely to want to remove
these different students from their testing pool.
The purpose of this research is to see if teachers’ perceptions, attitudes and
subsequent behaviors impact the under representation of African American students. This
2
study will focus on looking at teachers’ perceptions of African American students, their
awareness of traditional and alternative assessment tool for African American learners,
and their involvement in staff development that may help in identifying African
American learners for gifted programs.
Definitions
Attitudes can be defined as being formed when an individual comes to believe that
an item or a person possess desirable or undesirable traits or that they will bring about
desired or undesired outcomes (Fazio & Olson, 2003).
Perception is defined in the American Heritage Dictionary (1976) as an
impression in the mind of something discerned by the senses.
Stereotyping is a process of impression formation which begins with the
observation of a target person by the social perceiver. This observation leads to the
identification and categorization of the target’s behavior. Through a process of
attribution, this behavior categorization leads to a characterization or inference about the
actor, and these inferences may or may not be moderated by a process of correction in
which the perceiver considers other (perhaps situational) factors that might have
produced the behavior (Quinn, Macrae & Bodenhausen, 2003, p 68).
Prejudice is related to stereotyping. Prejudice is taking a stereotype and basing
one’s actions on the stereotype. For example, if a person were only described by a
female name, the stereotypes associated with being female can impact the perceiver’s
impression of that person, before they even meet. As it relates to this study, a teacher
knowing a student’s ethnicity could cause them to form certain prejudgments before even
meeting them (Quinn, Macrae & Bodenhausen, 2003).
3
Gifted Education is a program designed to educate students who are gifted.
Gifted is defined by the state of Kansas as performing or demonstrating the potential for
performing at significantly higher levels of accomplishment in one or more academic
fields due to intellectual ability, when compared to others of similar age (Kansas
Department of Education, 2010).
Special Education is a program designed to educate students with any of several
specific learning disabilities, health related disabilities or mental retardation. It must be
shown through screening results, review of student records, interviews with parents,
student and teachers, observation of the student, and aptitude and achievement tests that a
student has an exceptionality. A student cannot qualify for special education services if it
is determined that the sole reason for their learning deficit is due to lack of instruction in
reading or math or limited English proficiency (Kansas Department of Education, 2001).
Overview
Chapter two will discuss the background of the achievement gap, the over
representation of African American students in special education and under
representation in gifted education. It will also look at possible theoretical reasons and the
connection between education and these theoretical reasons. Chapter 3 will discuss the
method used to compile data, the sample of individuals, and the instruments and
procedures. Chapter 4 includes the analysis of the data and Chapter 5 answers the
question, “So what?”
4
Chapter 2
Literature Review
The literature review will address research on the possible causes for the under
representation of African American students in gifted education. It will also address
theoretical perspectives surrounding attitudes, perceptions and behaviors. Finally, it will
look at research about how teachers’ own attitudes and behaviors can affect the
educational outcome of their students.
There has been a lot of research on the under representation of African American
students in special education (Boone and King-Berry, 2007; Williams, 2007; Ford,
Grantham & Whiting, 2008). There has not been as much research on the underrepresentation of minority students in gifted education. Although this problem pervades
many minority groups, this research will focus on African Americans. African American
students compose 17.2% of school districts; however, they represent only 8.4% of those
identified as gifted according to the United States Department of Education (2002; cited
in Ford, et al, 2008). No Child Left Behind called for educational reform and called for
the implementation of many intervention and prevention programs, but the achievement
gap and under-representation of minority students in gifted programs persists (Ford, et al,
2008).
Research on Causes
Why do these problems still persist? The literature that is available on this subject
provides several reasons. First, the instruments used to measure the aptitude and
performance of ethnic minorities are not used accurately. Many instruments are not
normed for ethnic minority populations (Suinn & Borrayo, 2008). Second, Barton (2003;
5
cited in Ford, et al, 2008) found 14 factors in two categories that were said to be strongly
associated with the achievement gap.
The school influences included lack of rigor in curricula in schools serving
Black students, less access to technology-assisted instruction, having less
qualified teachers, having less experienced teachers, and lower levels of
feeling safe in school. Some of the before and after-school influences
were defined as factors in the home or community, including less family
participation, lower levels of parental availability to their children, lower
rates of parents reading to their children, and more frequent TV watching
by Black children. (p 217)
Ford, et al (2008) also looked at four theories that might help explain the
achievement gap. One is the attitude-achievement paradox (Ford, 1996; Ford & Harris,
1996). Simply stated, most people in the United States believe that if you work hard you
will be successful. For many young Black people, although they may say they believe
this, their performance does not support their verbal expressions, thus creating the
paradox. They may believe in the statement that hard work leads to success, but possibly
their life experiences have shown them (that what may be true for most people) will not
be true for them. Thus they may feel why put out the effort, just to be disappointed.
Ford (2008) suggested, secondly, that because African Americans were
involuntary immigrants (slaves forcibly brought over to the United States) there is a
propensity to resist assimilation to the dominant culture and to adopt an attitude of
resistance (Ogbu, 2004). This is referred to as secondary resistance and shows itself by
the minority group putting a low priority on the oppressor group’s values and beliefs.
6
Therefore, since mainstream White America places a high value on succeeding in school,
it is possible that secondary resistance leads to some of the achievement gap.
A third theory put forth by Ford (2008) relates to peer pressure, specifically the
pressure put on Black students to not act White (Ogbu, 2004). The findings she stated
found that “acting White” includes “getting good grades, being intelligent, speaking
standard English, dressing in certain ways, having White friends, and other attitudes and
behaviors. Many Black students are looked down upon if they “act White” and thus they
do not attempt to take full advantage of the academic programs made available to them,
including gifted education. “The desire to have friends and to be popular, as well as to
avoid alienation, isolation and rejection, plays a critical role in the decisions gifted Black
students make relative to staying success oriented and academically focused” (Ford, et al,
2008, p 223).
A final theory from this article by Ford (2008) is that of stereotype threat (Steele,
1999). “Stereotype threat is the threat of being viewed through the lens of a negative
stereotype and the associated fear of doing something that would inadvertently confirm
that stereotype” (Ford, et al, 2008, p 223). Because there are negative stereotypes about
Blacks pertaining to achievement and intelligence, and because many Blacks internalize
these negative stereotypes, they will perform more poorly on achievement and
intelligence tests than White students, even if their ability level is comparable to theirs.
In Williams’ (2007) study, one participant stated that she felt that many teachers
in the education system are not aware of the cultural differences of African American
students. This is a concern that should be taken seriously. An article in the New York
Times stated that 75% of all teachers are female (Simons, 2005). The US Department of
7
Education states that 86% of all elementary and secondary teachers are European
American whereas only 7%, in 1998, were African American. According to the National
Center for Educational Statistics (2007), in the 2005-2006 school year, 57.1% of the
overall US student population was White and 17.2% was African American. The
percentage of White teachers to White students is much more even than when the
percentage of African American teachers is compared to that of African American
students.
Theoretical Perspective
Why is it so important to consider the ratio of White teachers to Black students?
Does it really matter if teachers are different than their students? This researcher would
argue that the issue of in-group and out-group dynamics addresses this phenomenon. In
the field of social psychology there is a notion that individuals identify with groups for a
variety of reasons. In any setting there will be in-groups and out-groups (Hogg &
Abrams, 2003; Wright & Taylor, 2003). In the educational setting, the White female
teachers would be considered the in-group, because they make up the majority. The
Black students would be considered one of the out-groups. When this happens, the ingroup can wield a lot of power over the out-group. This is important because people are
generally motivated to maintain a positive self-view and similarly want to see the groups
that they belong to favorably. However, there is evidence that, in order to maintain a
positive view of one’s own group, an individual will maximize the positives about their
group, creating in-group favoritism. Beyond doing this, a person will not only minimize
positives about out-group members, but will even maximize what they see as apparent
faults or weaknesses (Goethals, 2003).
8
This is important to look at in the light of under representation of Black students
in gifted education; because the in-group has such power, it is important to look at what
their perceptions of these Black students are. This is critical because perceptions impact
attitudes, which in turn impact behavior. The behavior of teachers that could be impacted
is their ability or willingness to see exceptional, gifted qualities in their Black students.
In order to talk about in-group and out-group relations there are some terms that
must first be defined. Perceptions are known as stereotypes. According to Wright and
Taylor (2003) stereotypes are “beliefs, shared by the members of one group, about the
shared characteristics of another group” (Wright & Taylor, 2003, 362). Attitudes are
described as prejudice. According to the same authors, prejudice is a socially shared
judgment or evaluation of a group, including the feelings associated with that judgment.
Stereotyping and prejudice play a major role in understanding the complex
interrelationships between in-groups and out-groups and, more specifically, as discussed
here, the interrelationship between mainly White female teachers and the out-group of
African American students. “Stereotyping and prejudice involve a dehumanized view of
the other; members of the target group are not seen as individuals, but as representations
of the category” (Wright and Taylor, 2003, 363). According to several studies cited in an
article by Wright and Taylor, there is a connection between the relationship between
groups and the perception of having similar traits, values, attitudes. When the relations
are harmonious, the in-group is more likely to see the out-group in a more compatible
way; and when there is conflict and/or strain between the groups, then the view of the
out-group is decidedly more negative. This is important because it shows that external
experiences can impact internal feelings and perceptions. And these perceptions can
9
impact the in-group’s attitude toward an out-group and thus, how the in-group behaves
toward the out-group. According to Wright and Taylor (2003) inter group attitudes serve
not only to clearly define the position of groups in society, but also to justify that
placement and the treatment that goes along with that status. When people attribute
laziness, stupidity, or evil to low-status groups, it not only serves to explain their low
position, but also helps to justify their continued poor treatment.
An attitude is formed when an individual comes to believe that an item or a
person possesses desirable or undesirable traits or that they will bring about desired or
undesired outcomes (Fazio & Olson, 2003). There are many different avenues through
which attitudes can be formed. Some attitudes are formed because of some emotional
response to an attitude object. An example of this might be a person who once was
bullied by another student who happened to be African American. Because of the
emotional pain that caused, a person may have a negative emotional reaction to other
African Americans, especially if they are overbearing or pushy. Some attitudes are
formed just by mere exposure to an attitude object. If a person has been exposed to
something, even just by seeing it briefly, they tend to show at least a slight preference for
it. An example from education could very simply be that if the prevalent culture
represented is White, it may automatically be preferred over other cultures. And
sometimes attitudes can be impacted by the influence of external things; for example, a
person may have an intrinsically motivated positive attitude toward an object, but when
the behavior motivated by this positive attitude is rewarded externally, the intrinsically
positive attitude decreases (Fazio & Olson, 2003). This can be seen in a classroom where
token reward systems are overly used. If a student already loves reading, but is
10
constantly told that if they read they will get some type of reward, they may actually stop
reading as much.
The Motivation and Opportunity as Determinants of the attitude-behavior relation
(MODE) model gives some understanding of how and when attitudes will impact
behaviors (Fazio & Olson, 2003). Basically, this model posits that when an individual
has the motivation or energy and enough time to give to making a decision, he will make
a thorough consideration of all the facts and not default to attitudes in order to make a
decision about something. However, when a person is not motivated and/or does not
have energy, desire, or time to devote to the decision making process, that person will
default to attitudes to guide behavior. A simplified example might be if a person who is a
wonderful teacher, is single, with no children, and devotes much of her spare time outside
of school to planning lessons and learning ways to best meet the various needs of all the
students in her classroom. She gives time and attention to the strengths and weaknesses
of all her students and works hard to see each child as an individual. Then she gets
married and has a child with a serious medical condition that takes a lot of her time and
energy. At this point, although she is still the same great teacher, she will be more likely
to default to her attitudes to help guide decisions about her students. She may not have
the time or the energy (or motivation and opportunity) to give to the decisions teachers
are asked to make on a daily basis. Her motivation and opportunity is decreased and
therefore she defaults to what is comfortable to her. This may result in her not taking the
time to see exceptional qualities in a student that may show those qualities in an unusual
way.
11
This MODE model relates very well to the public school setting. Teachers are
often overwhelmed, over-worked, exhausted, and given limited time to make decisions
about what to do to help kids learn. Given these facts, it is no wonder that teachers
default to what they know. And since most American teachers are White females, their
attitudes and beliefs are often guided by their own limited exposure to the out-group, the
African American students.
Teacher Attitudes and Behavior
Up to this point the focus of this paper has been on general theories of social
psychology relating to attitudes and their potential impact on behavior. Now it is time to
make the connection between these general ideas and the specific context of how
teachers’ attitudes and perceptions may impact their behavior toward their students.
As stated previously, teachers wield a lot of power in the classroom (Hinnant,
O’Brien, & Ghazarian, 2009). They have the power to encourage or to devastate, to
empower or to weaken, to elevate or to deflate. How a teacher feels toward her students
can have impact on how she treats her students and ultimately impact their future success
or failure (Begeny, Eckert, Montarello & Storie, 2008; Hinnant, O'Brien & Ghazarian,
2009; Ladd & Linderholm, 2008; MkKown & Weinstein, 2008). In a study looking at
teacher perceptions of reading ability (Begeny, Eckert, Montarello & Storie, 2008), the
writers cited a study by Hoge and Coladarci (1989) which synthesized the results of 16
empirical studies conducted from 1971 – 1988, and found that there was a moderate to
strong association between teacher judgments and student achievement. Begeny and
colleagues pointed out that teacher judgments can have long-term effects because they
are used to make educational decisions about academic opportunities. Teacher judgments
12
are often cited in decisions related to the administration of gifted or special education
services.
Another study looked at the long-term impact of teacher expectations on
achievement in the early school years (Hinnant, O'Brien & Ghazarian, 2009). They
specifically addressed the domains of math and reading for young children (first, third
and fifth grades). They hypothesized that among other things, teachers underestimate the
academic abilities of minority children and those from low-income families. In this study
they found that teacher expectations had no significant impact on student performance in
reading 2 years later or 4 years later. However, they found that teacher expectations in
first grade math did show a significant impact on student performance in third and fifth
grades. When a teacher had a more negative view of a child’s performance than the
child’s actual math performance warranted, the child tended to perform less well in later
years. Visa versa; if the teacher had a more positive view of a child’s math performance
than the child’s actual math performance warranted, the child’s future math performance
tended to be better in the future years. These finding were slightly mediated by the
child’s family income. For children from families with average or low income, teacher
expectations were shown to have a significant impact on future math performance.
In yet another study by Ladd and Linderholm (2008), participants were asked to
watch a video of children in a classroom setting. The video was the same for each
participant, but some participants were told that the school shown in the video had been
rated as an “A”, “F”, or “typical” school, based on the school’s overall student
performance on achievement tests. What the researchers found was that when
participants thought they were viewing an “F” school they claimed to see more and
13
recalled more negative behaviors by the children than the participants who believed they
were viewing a “typical” school. They also tended to point out fewer positive behaviors.
They concluded that the automatic attitude activation effect was successfully applied in
the educational environment of their study.
The importance of this study is that it shows that when a person is primed to see
one thing, they are more likely to see it. When it comes to teacher expectations, teachers
who may have a less than positive view of certain-groups of students may be more likely
to see only what they expect to see. Since they are expecting to see lower performance on
tests, or less than enthusiastic involvement in the classroom, that is all they see. On top
of this, they tend to not see the positive aspects of these students’ actions in the
classroom.
One study by McKown and Weinstein (2008) looked at teacher expectations and
the classroom context and how they related to the achievement gap. They found that
teacher expectations are more closely linked to the achievement of African American
students than they are to the achievement of European American students. They also
found that there continues to be a lower expectation for African American and Latino
students and higher expectations for European American and Asian American students.
Kuklinski and Weinstein (2000; cited in McKown & Weinstein, 2008), found that
teachers who treat high and low achieving students very differently have a tendency to
hold more stable and rigid expectations for all students. They are not as willing to see
individual differences in students and judge students on things like ethnicity or social
class. Their research found that there was a .75 standard deviation discrepancy between
teacher expectations toward Black and Latino students and White and Asian-American
14
students. They showed that teachers tended to rate Black and Latino students who had
average prior achievement just below “low average” (1.9, with 2.0 being Low Average)
and White and Asian students with average prior achievement at just about average (2.8,
with a 3 being average). These finding were found to be statistically significant. The
final area this study addressed was what impact the teacher expectations have on the
year-end achievement of the students. In high-bias classrooms, teacher expectations are
found to account for .21 to .38 standard deviation discrepancy of year-end achievement
between stereotyped and non-stereotyped ethnic groups, with the average effect size
being d = .29. This means that in classrooms where the teacher may have rigid, longstanding expectations for students based on the students’ ethnicities, there could be
negative impacts on the stereotyped group even by the end of that school year. Imagine
if this happened multiple years for a student. The impact of these negative expectations
on a student with prior average achievement could be quite detrimental.
Summary
Research has shown that African American students are under-represented in
gifted education programs and they are over-represented in special education programs.
Parents of African American students feel that some possible reasons for this is that
teachers, school psychologists and other school personnel do not understand the culture
of these students. They also feel that sometimes teachers are making unilateral decisions
without, especially without input from parents. And finally, some parents feel that
teachers may be motivated to remove these “different” kids from their classrooms in
order to clarify their testing pool for government mandated testing.
15
These problems may persist for many possible reasons. One reason may be that
psychological testing used to determine qualification for special programs may not be
used accurately or appropriately on minority students. Also, schools that serve Black
students may lack rigor, may have less access to technology, may have less
qualified/experienced teachers and the students may have lower levels of feeling safe.
Some factors outside of school may be less family participation, lower levels of parent
availability, lower rates of parents reading to children, and more frequent television
watching by African American students.
Some other possible reason for the achievement gap may be that African
American students just don’t feel that it is possible for them to be successful. They know
that it is possible for some people, but they don’t feel that it is personally going to happen
for them, and therefore don’t put out the effort to be and do the best they can. They may
also be resistant to those things that are considered highly valued by White people, such
as education. There may be pressure to not act White and finally these students may have
heard stereotypes about themselves for so long that they begin to believe it themselves, or
become so anxious about inadvertently confirming the stereotype that they impair their
own performance.
As we look at this problem from the social psychology perspective one must
consider that most teachers in American schools are White, female (65.1%) with only 7%
being African American. The African American population of students is 17.2%. In this
sense, there is a definite feeling on in-groups and out-groups. The in-group, in this case
the White, female teachers, wield a lot of power and play a major role in deciding who
has access to gifted education programs. Their attitudes and beliefs about students may
16
play a vital role in the future success of their students. Experiences of teachers can
impact their feelings and perceptions and this can impact their behavior toward their
students. It has been shown that attributing negative attributes to a low-status group (in
this case, African American students) helps explain why they are disadvantaged and may,
in the mind of the high-status individual, justify their continued victimization. Basically,
the idea that “they” have brought this on themselves and there is not much we can do
about it.
Attitudes can be formed in various ways. They can be formed through negative
prior experiences, through mere exposure or because of other external factors. One
model states that attitudes impact behavior when there is not the motivation and/or the
opportunity to take the time to look past our attitudes and see each situation as unique and
deserving of special attention. Unfortunately, many teachers find themselves in
situations where they have too many students, too many responsibilities and not enough
time to devote to all the decisions that must be made. Therefore, they default to what
they know (or think they know) and may often make decisions that are not based on the
attributes on a specific student, but on the prejudices they may have about whatever
group that student may be a part of.
Research has found that teacher judgments can have a long-term effect on student
achievement. These judgments are often used to make decisions about the access a
student will have to special services (e.g., special education or gifted education). One
study found that teachers tend to underestimate the ability of minority and low-income
children. Another study showed that if teachers thought they were watching kids on a
video who were from a low-achieving school, they recalled more negative behaviors and
17
pointed our fewer positive behaviors than when teachers watched the same video but
were told that the kids were in an average-achieving school. This shows that when
teachers are primed to see negative, they are more likely to see it. And if a teacher
already has lower expectations for African American students, they will be more likely to
see the negatives and less likely to see the qualities of giftedness that may be present.
What is even more powerful is that it has been found that teachers’ expectations
are more closely linked to the achievement of African American students than to that of
European American students. However, teachers in this study rated European and Asian
American students as higher than average and African and Latino American students as
lower than low average, even though their actual abilities were comparable.
Research Questions
The purpose of this study is to address why African American students appear to
be under-represented in gifted programs. It will utilize the social psychological
perspective that perceptions (stereotypes) and attitudes (prejudice) affect teacher
behavior. This study will focus on five questions:
1. What are elementary teachers’ perceptions of African American learners?
2. What are elementary teachers’ perceptions of African American students
recommended for special education placement?
3. What are elementary teachers’ awareness of traditional assessment tools used
to place children in their school district’s gifted education and special
education programs?
18
4. What are elementary teachers’ awareness of alternative assessment tools used
to place African American learners in their school district’s gifted education
programs?
5. Do elementary school teachers participate in staff development classes offered
to assist teachers in correctly identifying African American learners for gifted
and special education placement?
19
Chapter 3
Methods
The study was conducted in an urban Midwest school district of which the
African American population is approximately 11%. In the public school district, there
are 49,000 students; of these, 10,909 (22%) are African American and 22, 010 (45%) are
White. Twenty-one elementary schools that receive Title I funds and have a minimum of
75% of their students who are eligible for free and reduced-cost meals were selected for
the study. These particular schools were chosen from this district because they represent
a majority of ethnic minority and poor students and the ethnic classification of teachers
were proportionate to that of the district.
Sample
Participants were teachers from 21 schools. Out of 461 possible participants,
there were 322 teachers (301 females, 21 males) who participated in the study. This is a
70% participation rate. The ethnic diversity reflected by the surveys did not exactly
reflect that of the school district, which has approximately 10% being from ethnic
minority groups. The ethnic diversity in this study was approximately 80% White and
20% Minority groups. The ethnic backgrounds represented included African American,
Asian American, Hispanic, Native American and White. Ages ranged from 22 – 62
years. Number of years teaching ranged from 1 – 35. Educational levels ranged from
Bachelor’s degree to one person with a Specialist’s degree.
Instrument
The instrument is a 25-item survey in Likert-type format developed by Dr.
Gwendolyn Mukes (See Appendix A). Questions were included based on the 5 research
20
questions for the study. Respondents rated each item on a continuum: Strongly Agree
(6), Somewhat Agree (5), Agree (4), Disagree (3), Somewhat Disagree (2), Strongly
Disagree (1). The questions are overt in nature thus face validity is sufficient.
Demographic information was requested at the end of the survey (See Appendix A).
Procedures
Participants were teachers at the identified elementary schools in the district who
volunteered to participate in the study. Surveys were brought to the schools and explained
at staff meetings by Dr. Gwendolyn Mukes. Participants were asked to take part in the
study, and a brief explanation of the research was imparted. Participants who agreed to
take part received a research packet containing a consent form (See Appendix B) and a
survey with demographic information requested at the end of the survey. After signing
the consent form the respondents were asked to respond to the survey form and provide
demographics. At 11 schools, the researcher had the participants fill out the surveys at
the meeting. At the remaining 10 schools the surveys were left and the researcher went
back and picked them up later.
21
Chapter 4
Results
Psychometric Analyses
Due to the fact that this is the first time this instrument was used, tests were
completed to determine the reliability of the measure. An item analysis was performed to
determine missing values and extreme skewness of individual items. One question was
answered by so few respondents that it was eliminated from the study. Observation of
the results of the item analysis indicated that the remaining items could be statistically
analyzed; therefore, an exploratory factor analysis was conducted. Using the typical
cutoff of an Eigenvalue (Kaiser, 1970) of 1.0 generated eight factors, 3 of which were
meaningful and accounted for 34% of the variance. Another exploratory factor analysis
was performed; this time the 3 factors were forced to get factor loadings. High loading
items for each of 3 factors were chosen to determine which items compose each factor.
A Cronbach Alpha was conducted to determine inter-item reliability for each factor.
The first factor was misidentification of African Americans, indicating that high
scores suggested that teachers perceive that African Americans are more likely to be
over-identified for special education and under-identified for gifted education. There
were seven items for this factor. The Cronbach Alpha for this factor was r = .72.
The second factor was ability to learn for African Americans. High scores
indicated that teachers perceived that African American learners have a comparable
ability to learn as any other ethnic group. There were five items for this factor. The
Cronbach Alpha for this factor was r = .56.
22
The third factor was adequate teacher training. High scores in this factor
indicated that teachers perceived that they were adequately trained to deal with diverse
populations. There were four items for this factor. The Cronbach Alpha for this factor
was r = .67.
It was decided that the Cronbach Alphas were sufficient to include these three
factors as being measured by the instrument. Eight items from the survey did not load on
any factor and therefore were not included on the analysis. See Appendix C for results of
statistical analyses.
Analyses Addressing Research Questions
The research questions for this study were:
1. What are elementary teachers’ perceptions of African American learners?
2. What are elementary teachers’ perceptions of African American students
recommended for special education placement?
3. What are elementary teachers’ awareness of traditional assessment tools used
to place children in their school district’s gifted education and special
education programs?
4. What are elementary teachers’ awareness of alternative assessment tools used
to place African American learners in their school district’s gifted education
programs?
5. Do elementary school teachers participate in staff development classes offered
to assist teachers in correctly identifying African American learners for gifted
and special education placement?
23
Based on the 3 factors that were generated from the exploratory factor analysis,
only three of the research questions were really answered by the survey questions.
Questions 1, 2, and 5 are able to be discussed based upon results from the survey, but
questions 3 and 4 did not appear to adequately be covered on the survey. Question 1 is
described from this point on as African American students’ ability to learn. Question 2 is
described as misrepresentation, and question 5 is described as adequate teacher training.
Descriptive statistics. Scores are based on a 6 – point Likert scale from 6 =
Strongly Agree to 1 = Strongly Disagree. Because several of the demographic variables
included a broad range of numbers, categories were formed for analyses. The ages of the
participants ranged from 22 to 62; they were summarized into two groups; those who
were 35 years of age or less and those who were 36 years of age or more. Years of
teaching ranged from 1 to 35 years. This variable was also categorized into two groups:
those who taught for 15 years or less and those who taught 16 years or more. The
numbers of teachers in the several ethnic minorities were small; thus, all minority
teachers were categorized into one group. Finally, the three groups of teachers who had
more than a bachelor’s degree were also small; thus, all teachers who had schooling
beyond the bachelor’s degree were categorized into one group. See Table 1 for Means
and Standard Deviations for each factor for all individual variables. Because each factor
had a different number of items that loaded, means were calculated by totaling the
number of points for each item of the factor and then dividing by the number of items in
each factor. In order to interpret the mean scores, it is important to note that scores from 1
to 3 would designate disagreement with the factor, and scores from 4 to 6 would indicate
agreement with the factor.
24
Table 1
Numbers, Means and Standard Deviations of Each Factor for Each Demographic Variable
N
Misidentification
M (SD)
Ability to Learn
M (SD)
Adequate Training
M (SD)
Gender
M
F
16
203
3.2 (.63)
3.6 (.78)
2.6 (.56)
2.7 (.57)
2.5 (.51)
2.5 (.49)
Age Groups
22 – 35
36 +
125
89
3.4 (.68)
3.8 (.83)
2.7 (.57)
2.8 (.58)
2.6 (.48)
2.4 (.49)
Years Teaching
1 - 15
16 +
162
55
3.5 (.75)
3.8 (.81)
2.7 (.58)
2.7 (.56)
2.5 (.49)
2.4 (.51)
Ethnicity
White
Minority
163
40
3.5 (.81)
3.9 (.62)
2.7 (.54)
2.9 (.67)
2.5 (.50)
2.6 (.46)
Education
Bachelor’s
Bachelor’s Plus
133
84
3.5 (.69)
3.7 (.88)
2.7 (.54)
2.8 (.61)
2.5 (.49)
2.5 (.49)
Multicultural
Training
Yes
No
191
27
3.6 (.77)
3.7 (.79)
2.7 (.58)
2.6 (.57)
2.2 (.49)
2.5 (.48)
Total
329
3.6 (.78)
2.7 (.56)
2.5 (.51)
Multivariate Analysis of Variance. Because there were three dependent
variables (misidentification, ability to learn, and adequate teacher training), a MANOVA
was used for each of the independent variables. .
The first MANOVA was run for the independent variable of age. The analysis
indicated a significant finding for misidentification F(1, 212) = 12.02, p = .001. The
older age group was more likely to perceive that African American students are
25
misidentified for special education and gifted programs. Adequate teacher training was
also significant F(1,212) = 10.95, p = .001. The younger age group perceived that they
have had more adequate teacher training in how to deal with students from different
cultural backgrounds (see Table 2).
Table 2
MANOVA for Age and Misidentification, Ability to Learn, and Adequate Training
Factor
df
F
P
eta²
Misidentification
1
12.02
.001
.05
Ability to Learn
1
.34
.56
.00
Adequate Training
1
10.94
.001
.05
within-group error
212
The second MANOVA was run for the independent variable of ethnicity. The
results indicated a significant finding for misidentification F(1, 201) = 6.32, p = .01. The
minority group of teachers was more likely to perceive that African American students
are misidentified for special education and gifted programs. There was also a significant
finding for ability to learn F(1, 201) = 4.57, p = .03. Again, the minority group of
teachers perceived that African American have a comparable ability to learn as any other
ethnic group (see Table 3).
26
Table 3
MANOVA for Ethnicity and Misidentification, Ability to Learn, and Adequate Training
Factor
df
F
P
eta²
Misidentification
1
6.32
.01
.03
Ability to Learn
1
4.57
.03
.02
Adequate Training
1
1.51
.22
.00
within-group error
201
A third MANOVA was run for the independent variable of number of years
taught. A significant finding resulted for misidentification, F(2, 215) = 4.27, p = .04.
The group of teachers that taught 16 years or longer perceived that African American
students are more likely to be misidentified for special education and gifted programs
(see Table 4).
Table 4
MANOVA for Years Taught and Misidentification, Ability to Learn, and Adequate Training
Factor
df
F
P
eta²
Misidentification
1
4.27
.04
.02
Ability to Learn
1
.06
.81
.000
Adequate Training
1
2.16
.14
.01
within-group error
215
A fourth MANOVA was run for the independent variable of gender. A
significant finding resulted for misidentification F(1, 217) = 4.10, p = .044. Females
were more likely to perceive that African American students are misidentified for special
27
education and gifted programs (see Table 5). Because there were so few males in the
study, these results need to be addressed cautiously.
Table 5
MANOVA for Gender and Misidentification, Ability to Learn, and Adequate Training
Factor
df
F
P
eta²
Misidentification
1
4.10
.04
.02
Ability to Learn
1
.43
.52
.00
Adequate Training
1
.13
.72
.00
within-group error
217
A fifth and sixth MANOVA were run for the independent variables of educational
level of the teachers and the amount of multicultural training on the job teachers received.
There were no significant findings with any of the factors and these independent
variables.
28
Chapter 5
Discussion
Theoretical Implications
In looking back at what was found, three research questions emerged that seem to
have evoked some type of significant findings. The first research question was, “What
are elementary teachers’ perception of African American learners?” Overall, it was
found that, generally speaking, the teachers surveyed for this study felt that African
American learners do not have a comparable ability to learn as any other ethnic group.
Based on this study it could be said that teachers, despite gender, age, length of teaching
service, degree attained, training received or ethnicity perceive that African American
students have a more difficult time learning than do students from other ethic groups.
Minority teachers were slightly less likely to feel this way, but their average scores still
fell in about the disagreement range as all other demographic groups.
This could be incredibly meaningful especially when one looks back at the
discussion about in-group and out-group dynamics (Hogg & Abrams, 2003; Wright &
Taylor, 2003). If the in-group, in this case, the White teachers, feel, even slightly, that
African American learners might not have a comparable ability to learn as other ethnic
groups, why would they see qualities of giftedness? Also, if the White teachers have this
perception, it may impact their behavior negatively. In their minds this may serve to
justify African American’s placement in society as a whole and especially their nonplacement in gifted programs, as cited by Wright and Taylor (2003).
Begeny and Colodarci (1989) found that teachers’ judgments are often vital in
determining access of students to academic programs. If teachers do not see African
29
American students as comparable to other students, they are certainly not going to
recommend them for placement in more challenging programs, (e.g., gifted education).
The second research question found to have a significant finding was, “What are
elementary teachers’ perceptions of African American learners recommended for special
education placement?” Overall scores showed that, generally speaking, the teachers
surveyed for this study felt that African American students are not misidentified when
they are placed in special education programs. Even though African American students
are represented at a higher ratio in special education programs than other ethnic groups,
teachers indicate that this is appropriate. However, the results did show that teachers
over the age of 35, teachers who have taught 16 plus years, and minority teachers are
more likely to perceive that African American learners are misidentified for special
education. What this says is that many teachers feel that special education is the
appropriate placement for African American students who are low performers .
A question then that might be raised by the findings from the first two questions is
this: If teachers feel that African American students cannot learn as well as other
students, AND they feel that they are not being misidentified for special education, what
chance do these students have? If the main people in their lives who can, and do, make
educational decisions that impact student futures simply think they are not capable of
doing any better, why should they try. Ford (2008) discussed the attitude – achievement
paradox-- that basically suggests that, although African American students have the
notion that hard work should reap success and they can repeat the mantra of the
American dream, they still do not tend to personalize it. They tend to believe that
although they know it’s true that hard work leads to success, they do not see it around
30
them in their daily lives and therefore do not really think that it applies to them
personally. And if that is the case, why should they put out the extra effort it takes to be
successful. Also based on literature (Begeny, Eckert, Montarello & Storie, 2008;
Hinnant, O'Brien & Ghazarian, 2009; Ladd & Linderholm, 2008; McKown & Weinstein,
2008), there is evidence that what a teacher thinks about a student can impact their future
performance even years into the future. If the findings from this study are accurate, this
issue could turn out to be a big part of the achievement gap puzzle.
The final research question that showed any significance was, “Do elementary
school teachers participate in staff development classes offered to assist teachers in
correctly identifying African American learners for gifted and special education
placement?” Overall scores showed that across the board, teachers surveyed for this
study feel that they have not adequately participated in staff development designed to
assist teachers in correctly identifying African American learners for gifted or for special
education. The younger age group teachers felt somewhat as though they have had more
adequate training in this area than the older age teachers. Other than that, there was no
really difference between any other of the groups.
This suggests that younger teachers have had more opportunities to learn about
cultural differences because this topic has come more to the forefront in just the last few
years. This may or may not mean that these teachers are actually better at identifying
African American students for special education or gifted placements.
What does this all mean for educators now? The MODE model (Fazio & Olson,
2003) addresses the predicament in which many teachers find themselves. With everincreasing class sizes, more diverse populations, fewer resources in some cases, and
31
responsibilities outside of school, teachers often do not have the motivation and/or
opportunity to make the informed and thoughtful decisions about students that they may
really know very little about. They may resort to using their predetermined perceptions
and attitudes to make vital academic decisions. These decisions can impact students for
years to come. The study by Hinnant, et al. (2009) indicated that a teacher’s view of a
student’s math performance impacted their actual math performance even up to 4 years
later. This was especially seen in families with average to low incomes, which includes
many African American students.
The perceptions, attitudes, and behaviors of teachers play a vital role in the
performance of all students. This study provides evidence that it could be possible that
some teachers feel that African American learners are not as capable as other students. It
also suggests that some teachers believe the most appropriate placement for lowachieving African American students is in special education. And it also has shown that
teachers may not be getting adequate training to recognize qualities of giftedness in
African American learners. This could be a lethal combination when it comes to the
prognosis of the representation of African American students in gifted education.
Limitations
There are several limitations to this study, as with any study. One of the
limitations was the data collection process. The surveys were briefly explained and then
left at the building sites to be filled out at a later time. If this study were to be done
again, the survey should probably be filled out at the time that they are explained and
handed out.
32
Another serious limitation is the newness of the instrument. It turned out that
several of the questions were not used in the final analysis because they did not really get
to the heart of what the study was about. Also, the researchers feel that even the
questions that were used may not have really measured perceptions and attitudes as well
as they would have liked. The instrument needs to be revisited and worked on for
reliability and validity. Also, the Likert scale was somewhat confusing because in the
middle it went from 4 = Agree to 3 = Disagree to 2 = Somewhat Disagree. Some of the
respondents remarked on their survey that it should have gone from Agree, to Somewhat
Disagree, to Disagree. Because of this, it appeared that sometime during the survey they
realized the Likert numbers were not what they thought they were and went back and
changed their responses.
Recommendations for Future Research
One possible recommendation for future research would be to find out why some
teachers see African American learners as having a less comparable ability to learn as
other ethnic groups. Another might be that if the findings of this survey are accurate,
what can be done to change teachers’ perceptions of African American students?
Another possible aspect to look into would be to see these apparent attitudes about
African American learners is really related to ethnicity, or is it possibly more related to
socioeconomic status. Could it be teachers feel this way about learners who come from
disadvantaged economic situations, regardless of ethnicity? Finally, it would be
recommended the instrument be further developed to build more reliable and valid
questions and then see if the results attained are the same.
33
REFERENCES
34
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37
APPENDICES
38
Appendix A
WICHITA STATE UNIVERSITY
Department of Curriculum and Instruction
CONSENT FORM
General Information
You are invited to participate in a study investigating the approaches used for the
placement of African American and Hispanic learners in the gifted and special education
programs in your school district. The title of the study is: The Identification of Minority
Learners In Public Schools.
Participation in this study is not mandatory and is strictly voluntary. Your
decision to participate will not affect your future relationship with Wichita State
University. If you decide to participate, please be aware that you may withdraw from the
study at any time. Information obtained in this study will remain confidential but the
conclusive findings will be shared with all.
If you have any questions at this time or during the study, you may contact Dr.
Gwendolyn F. Mukes at Wichita State University, Department of Curriculum and
Instruction (316) 978.6298. Results of this investigation will be provided to you in
written form, following the completion of the study.
You will be given a copy of this consent form to keep. You are under no
obligation to participate in this study. Your signature indicates that you have read the
information provided above and have voluntarily agreed to participate.
____________________________________
Participant’s Signature
Date
______________________________
Signature of Investigator Date
Wichita State University - Wichita, Kansas 67260-0028 Telephone (316) 978.3322 Fax: (316) 978.6935
www.wichita.edu
39
Appendix B
Questionnaire for Teachers
Directions: Please indicate your responses concerning your perceptions about the problems
related to identifying gifted students who are African American or Hispanic using the following
code:
6 = Strongly Agree; 5 = Somewhat agree; 4 = Agree; 3 = Disagree; 2 = Somewhat Disagree; 1 =
Strongly Disagree
1. Teachers in my building are encouraged to use alternative assessments
to identify gifted traits in ethnic minority students.
6 5
4 3 2 1
2. The standard intelligence testing has limited the participation of students
from culturally diverse backgrounds.
6 5 4 3 2 1
3. As a result of the training I received, I believe that all children, regardless of
race and/or culture, are intelligent and have the capacity to have gifted
qualities.
6 5 4 3 2 1
4. Special education is the correct placement for ethnic minority students who
are low academic achievers.
6 5 4 3
5. I am concerned that placing ethnic minority males in existing gifted
programs will lower the quality of those programs.
6
2 1
5 4 3 2 1
6. The WISC-R is an excellent alternative assessment tool that is accurate
in its assessment of all children for gifted education.
6 5 4 3 2 1
7. My formal education has prepared me to work with students whose cultures
are different from mine.
6 5 4 3 2 1
8. Teachers often do not recognize indicators of potential giftedness in ethnic
Minority males.
6 5 4 3 2 1
9. Intellectual giftedness is not valued by some cultural groups and, therefore,
their children are not encouraged to do well in school.
6 5 4 3 2 1
10. It is necessary to staff an ethnic minority student for special education
classes when he or she demonstrates an inability to participate in an
academic setting.
6 5 4 3 2 1
11. I am knowledgeable about alternative assessment identification tools
that can be used to identify gifted traits in ethnic minority students.
6 5 4 3
2 1
12. My school district offers staff development classes that address inclusion
of diverse populations.
6 5 4 3 2 1
40
13. Many potentially gifted ethnic minority males are not correctly identified
because of their lackluster performance in the classroom.
6 5 4 3 2 1
14. It is the responsibility of psychologists, not teachers, to identify the correct
alternative assessment tools.
6 5 4 3 2 1
15. Traditional methods used to identify students have relied primarily on
intelligence testing.
6 5
4 3 2 1
16. I do not feel that I was given adequate training in college to handle different
cultures in my classroom.
6 5 4 3 2 1
17. Unacceptable behavior may be an indication that alternative assessment
should be used to diagnose needs of ethnic minority males.
6 5 4 3 2 1
18. Traditional IQ tests do not discriminate against minority students.
6 5
4 3 2 1
19. Due to unawareness of prejudices, teachers may not nominate ethnic
Minority male learners for gifted education placement.
6 5 4 3
2 1
20. The standard intelligence tests have limited the participation of students
from culturally diverse backgrounds.
6 5 4 3
2 1
21. I have taken classes that prepared me to teach children from different
cultures.
6 5 4 3 2 1
22. Teachers often over-recognize indicators for special education placement
of ethnic minority students.
6 5 4 3 2 1
23. Alternative assessments can determine if an ethnic minority student is
gifted.
6 5 4 3 2 1
24. Parents often do not provide stimulating early home environments; thus,
students from ethnic minority groups often enter school at a disadvantage
and are unlikely to catch up.
6 5 4 3 2 1
25. Differences in background experiences often hinder the development of
giftedness in ethnic minority males.
6 5 4 3 2 1
41
Demographic Information
Gender:
M
F
Ethnicity: African American
American
Number of years as a teacher
Asian American
Other…………
Age at last birthday
European American
Hispanic
Native
.
Grade taught
School
Year
My bachelor’s degree was received from
Have you ever taken any course or staff development that addressed alternative testing
procedures? Yes No
If you answered Yes, how many courses have you taken?
Have you ever taken a multicultural education course during your pre-professional training? Yes
No
Staff development on multicultural education? Yes
42
No
Appendix C
Psychometric Analyses
Factor Analysis
Communalities
q1
Initial
.194
Extraction
.289
q2
.383
.399
q3
.222
.517
q4r
.171
.239
q5r
.143
.162
q7
.380
.610
q8
.545
.587
q9r
.389
.482
q10r
.252
.324
q11
.164
.213
q12
.267
.538
q13
.303
.365
q14r
.226
.318
q15
.245
.243
q16
.316
.372
q17
.308
.348
q18r
.303
.383
q19
.352
.352
q20
.506
.579
q21
.372
.465
q22
.327
.390
q23
.210
.275
q24r
.493
.605
q25r
.446
.768
Extraction Method: Principal Axis Factoring.
43
44
2.113
1.508
1.260
1.183
1.094
1.022
.910
.879
.841
.788
.751
.706
.687
.635
.617
.572
.498
.474
.404
.316
.295
.278
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1.160
1.229
1.317
1.682
1.974
2.077
2.385
2.572
2.648
2.862
2.942
3.130
3.285
3.504
3.664
3.792
4.258
4.557
4.928
5.249
6.285
8.803
9.260
% of Variance
16.437
Extraction Method: Principal Axis Factoring.
2.222
3
Total
3.945
2
Factor
1
Initial Eigenvalues
100.000
98.840
97.610
96.293
94.612
92.638
90.561
88.176
85.605
82.957
80.095
77.153
74.022
70.737
67.233
63.569
59.777
55.519
50.962
46.034
40.785
34.500
25.697
Cumulative %
16.437
.455
.507
.635
.692
.873
1.574
1.682
Total
3.409
45
1.895
2.111
2.644
2.883
3.638
6.557
7.007
% of Variance
14.205
40.939
39.044
36.933
34.289
31.406
27.768
21.211
Cumulative %
14.205
Extraction Sums of Squared Loadings
Total Variance Explained
.760
.847
.913
.975
1.089
1.327
1.373
Total
2.542
3.165
3.528
3.803
4.063
4.536
5.531
5.721
% of Variance
10.592
40.939
37.774
34.247
30.443
26.380
21.844
16.313
Cumulative %
10.592
Rotation Sums of Squared Loadings
Factor Matrix(a)
Factor
1
2
3
4
5
6
7
8
q1
-.074
.059
.231
.363
.132
.098
.251
-.069
q2
.531
.234
.032
.094
-.132
-.110
.151
.020
q3
.068
-.048
.347
-.362
-.187
.250
.378
.133
q4r
.074
.391
.116
-.018
-.166
-.030
-.108
.165
q5r
-.086
.169
.201
-.078
-.207
.176
-.037
-.063
q7
-.224
-.209
.617
-.049
-.023
-.343
-.059
.104
q8
.722
.151
.041
.111
.117
-.060
-.056
.096
q9r
-.482
.325
.091
-.133
-.064
-.075
.115
-.308
q10r
.028
.335
.254
.092
-.206
.203
-.198
.126
q11
-.020
-.178
.111
.317
.148
-.127
.170
.036
q12
-.060
-.249
.404
.444
.088
.322
-.007
-.013
q13
.497
-.179
.112
-.124
.134
-.015
.043
.194
q14r
-.031
.308
.013
.270
-.326
-.034
.014
.203
q15
.345
-.082
.227
-.252
.011
-.017
-.020
.043
q16
.292
.101
-.395
-.026
-.004
.124
.324
.009
q17
.519
-.159
.032
-.135
.093
.151
.005
.049
q18r
.361
.347
.133
.026
-.010
.133
-.105
-.293
q19
.531
.062
.088
-.025
.207
-.062
-.041
-.100
q20
.660
.306
.138
.096
-.021
-.014
.031
-.141
q21
-.192
-.144
.609
-.076
-.028
-.078
.077
-.133
q22
.458
.254
.151
.048
.039
-.292
.067
-.004
q23
.333
-.071
.191
-.217
.127
.162
-.154
-.099
q24r
-.394
.582
.059
-.127
.230
-.127
.138
.053
q25r
-.462
.495
.122
-.069
.462
.188
-.056
.198
Extraction Method: Principal Axis Factoring.
a Attempted to extract 8 factors. More than 25 iterations required. (Convergence=.006). Extraction was terminated.
Factor Transformation Matrix
Factor
1
1
2
3
4
5
6
7
8
.784
-.231
-.330
.396
.004
-.058
.239
.075
2
.402
-.236
.593
-.352
.506
-.139
-.162
-.073
3
.202
.757
.088
-.016
.294
.373
.200
.336
4
.090
-.097
-.177
.046
.134
.737
-.374
-.499
5
.082
-.014
.641
.218
-.512
.254
.387
-.241
6
-.357
-.489
.003
.114
.424
.337
.500
.277
7
.137
-.260
.078
-.212
-.399
.326
-.368
.680
8
-.152
.037
.289
.781
.197
-.101
-.449
.177
Extraction Method: Principal Axis Factoring.
Rotation Method: Varimax with Kaiser Normalization.
46
Factor Analysis
Communalities
q1
Initial
.194
Extraction
.045
q2
.383
.335
q3
.222
.079
q4r
.171
.175
q5r
.143
.076
q7
.380
.420
q8
.545
.545
q9r
.389
.336
q10r
.252
.157
q11
.164
.037
q12
.267
.141
q13
.303
.295
q14r
.226
.089
q15
.245
.183
q16
.316
.238
q17
.308
.304
q18r
.303
.249
q19
.352
.292
q20
.506
.558
q21
.372
.492
q22
.327
.286
q23
.210
.147
q24r
.493
.448
q25r
.446
.326
Extraction Method: Principal Axis Factoring.
47
48
2.113
1.508
1.260
1.183
1.094
1.022
.910
.879
.841
.788
.751
.706
.687
.635
.617
.572
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
.404
.316
.295
.278
20
21
22
23
24
1.160
1.229
1.317
1.682
1.974
2.077
2.385
2.572
2.648
2.862
2.942
3.130
3.285
3.504
3.664
3.792
4.258
4.557
4.928
5.249
6.285
8.803
9.260
% of Variance
16.437
Extraction Method: Principal Axis Factoring.
.498
.474
19
2.222
3
Total
3.945
2
Factor
1
Initial Eigenvalues
100.000
98.840
97.610
96.293
94.612
92.638
90.561
88.176
85.605
82.957
80.095
77.153
74.022
70.737
67.233
63.569
59.777
55.519
50.962
46.034
40.785
34.500
25.697
Cumulative %
16.437
1.428
1.510
Total
3.314
49
5.951
6.291
% of Variance
13.810
26.053
20.101
Cumulative %
13.810
Extraction Sums of Squared Loadings
Total Variance Explained
1.513
1.578
Total
3.161
6.305
6.577
% of Variance
13.171
26.053
19.748
Cumulative %
13.171
Rotation Sums of Squared Loadings
50
Factor Matrix(a)
Factor
1
2
3
q1
-.067
.072
.188
q2
.531
.229
.013
q3
.061
-.033
.273
q4r
.080
.399
.095
q5r
-.082
.187
.185
q7
-.215
-.158
.591
q8
.726
.129
.019
q9r
-.465
.336
.083
q10r
.033
.332
.214
q11
-.019
-.161
.105
q12
-.055
-.185
.322
q13
.492
-.200
.113
q14r
-.027
.297
-.008
q15
.342
-.091
.240
q16
.284
.071
-.390
q17
.517
-.187
.031
q18r
.357
.332
.106
q19
.531
.042
.085
q20
.670
.306
.122
q21
-.196
-.113
.664
q22
.454
.244
.139
q23
.326
-.080
.187
q24r
-.370
.557
.035
q25r
-.401
.401
.072
Extraction Method: Principal Axis Factoring.
a 3 factors extracted. 9 iterations required.
51
Rotated Factor Matrix(a)
Factor
1
2
3
q1
-.007
.124
.173
q2
.545
.115
-.156
q3
.121
.016
.254
q4r
.158
.386
-.030
q5r
-.006
.235
.143
q7
-.082
.021
.642
q8
.719
-.018
-.165
q9r
-.376
.432
.086
q10r
.133
.357
.109
q11
-.016
-.127
.144
q12
.001
-.095
.363
q13
.471
-.265
.058
q14r
.015
.287
-.079
q15
.374
-.104
.180
q16
.186
-.074
-.445
q17
.476
-.276
-.029
q18r
.417
.269
-.059
q19
.536
-.047
-.039
q20
.717
.185
-.101
q21
-.039
.076
.696
q22
.505
.173
-.025
q23
.347
-.101
.130
q24r
-.265
.614
-.035
q25r
-.308
.479
.047
Extraction Method: Principal Axis Factoring.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 5 iterations.
Factor Transformation Matrix
Factor
1
2
1
2
3
.958
-.200
-.205
.144
.955
-.258
3
.248
.218
.944
Extraction Method: Principal Axis Factoring.
Rotation Method: Varimax with Kaiser Normalization.
52
Reliability
Scale: misidentification
Reliability Statistics
Cronbach's
Alpha Based on
Standardized
Items
.718
Cronbach's
Alpha
.721
N of Items
7
Case Processing Summary
N
Cases
%
Valid
Excluded(a
)
Total
238
73.5
86
26.5
324
100.0
a Listwise deletion based on all variables in the procedure.
Inter-Item Correlation Matrix
q2
q8
q13
q17
q18r
q19
q22
q2
1.000
.419
.179
.282
.268
.206
.317
q8
.419
1.000
.334
.220
.295
.456
.409
q13
.179
.334
1.000
.305
.046
.310
.253
q17
.282
.220
.305
1.000
.134
.234
.170
q18r
.268
.295
.046
.134
1.000
.221
.281
q19
.206
.456
.310
.234
.221
1.000
.269
q22
.317
.409
.253
.170
.281
.269
1.000
Item Statistics
q2
Mean
3.48
Std. Deviation
1.311
q8
3.39
1.441
238
q13
3.78
1.227
238
q17
3.69
1.138
238
q18r
4.24
1.292
238
q19
3.17
1.352
238
q22
3.32
1.151
238
53
N
238
Item-Total Statistics
q2
Scale Mean if
Item Deleted
21.58
Scale Variance
if Item Deleted
22.573
Corrected
Item-Total
Correlation
.449
Squared
Multiple
Correlation
.248
Cronbach's
Alpha if Item
Deleted
.684
q8
21.68
20.092
.597
.390
.642
q13
21.29
23.884
.375
.208
.702
q17
21.38
24.616
.352
.161
.707
q18r
20.83
24.067
.328
.152
.714
q19
21.90
22.192
.460
.257
.681
q22
21.75
23.421
.462
.232
.683
54
Reliability
Scale: difficulty learning
Case Processing Summary
N
Cases
Valid
Excludeda
Total
%
88.9
11.1
100.0
288
36
324
a. Listwise deletion based on all
variables in the procedure.
Reliability Statistics
Cronbach's
Alpha Based
on
Standardized
Items
.557
Cronbach's
Alpha
.557
N of Items
5
Item Statistics
q9r
q24r
q25r
q4r
q10r
Mean
3.53
3.34
3.18
4.64
4.50
Std. Deviation
1.394
1.381
1.198
1.264
1.263
N
288
288
288
288
288
Inter-Item Correlation Matrix
q9r
q24r
q25r
q4r
q10r
q9r
1.000
.322
.258
.076
.080
q24r
.322
1.000
.555
.149
.084
q25r
.258
.555
1.000
.088
.071
55
q4r
.076
.149
.088
1.000
.324
q10r
.080
.084
.071
.324
1.000
Item-Total Statistics
q9r
q24r
q25r
q4r
q10r
Scale Mean if
Item Deleted
15.66
15.85
16.01
14.55
14.69
Scale
Variance if
Item Deleted
10.700
9.503
10.683
11.607
11.878
Corrected
Item-Total
Correlation
.291
.457
.406
.243
.209
56
Squared
Multiple
Correlation
.116
.351
.315
.120
.109
Cronbach's
Alpha if Item
Deleted
.519
.411
.454
.543
.561
Reliability
Scale: adequate training
Case Processing Summary
N
Cases
Valid
Excludeda
Total
%
96.9
3.1
100.0
314
10
324
a. Listwise deletion based on all
variables in the procedure.
Reliability Statistics
Cronbach's
Alpha Based
on
Standardized
Items
.675
Cronbach's
Alpha
.672
N of Items
4
Item Statistics
q7
q21
q12
q16r
Mean
4.5764
4.6433
4.3885
3.9268
Std. Deviation
1.23664
1.14458
1.24941
1.38143
N
314
314
314
314
Inter-Item Correlation Matrix
q7
q21
q12
q16r
q7
1.000
.462
.256
.427
q21
.462
1.000
.334
.331
57
q12
.256
.334
1.000
.244
q16r
.427
.331
.244
1.000
Item-Total Statistics
q7
q21
q12
q16r
Scale Mean if
Item Deleted
12.9586
12.8917
13.1465
13.6083
Scale
Variance if
Item Deleted
7.624
8.090
8.560
7.453
Corrected
Item-Total
Correlation
.521
.508
.354
.444
58
Squared
Multiple
Correlation
.302
.276
.137
.216
Cronbach's
Alpha if Item
Deleted
.560
.573
.668
.614
No Content
59