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The School Attitude Assessment Survey-Revised: A New Instrument to Identify Academically Able Students Who Underachieve



The purpose of this study was to design a psychometrically sound instrument to measure adolescents' attitudes toward school, attitudes toward teachers, goal-valuation, motivation, and general academic self-perceptions that could be used to explore the underachievement of academically able secondary school students. The final School Attitude Assessment Survey-Revised (SAAS-R) instrument consisted of 35 questions, each of which being an indicator of one of the five factors. The final model exhibited reasonable fit, X2(550) = 1,581.7, Comparative Fit Index = .91, Tucker Lewis Index = .92, root mean square error of approximation = .059, standardized root mean squared residual = .057. The scores in this study showed internal consistency reliability coefficient of at least .85 on each of the five factors. In addition, four of the five factors of the SAAS-R appear to differentiate gifted achievers from gifted underachievers. It is the authors' hope that the SAAS-R will allow researchers to more fully understand the relationship between these five factors and underachievement in gifted and nongifted populations.
University of Connecticut
The purpose of this study was to design a psychometrically sound instrument to measure
adolescents’ attitudes toward school, attitudes toward teachers, goal-valuation, motiva
tion, and general academic self-perceptions that could be used to explore the
underachievement of academically able secondary school students. The final School
Attitude Assessment Survey–Revised (SAAS-R) instrument consisted of 35 questions,
each of which being an indicator of one of the five factors. The final model exhibited rea-
sonable fit, χ
(550) = 1,581.7, Comparative Fit Index = .91, Tucker Lewis Index = .92,
root mean square error of approximation = .059, standardized root mean squared residual
= .057. The scores in this study showed internal consistency reliability coefficient of at
least .85 on each of the five factors. In addition, four of the five factors of the SAAS-R
appear to differentiate gifted achievers from gifted underachievers. It is the authors’hope
that the SAAS-R will allow researchers to more fully understand the relationship
between these five factors and underachievement in gifted and nongifted populations.
Keywords: underachievement; instrument validation; motivation; gifted underachiever;
high school students
Every teacher knows at least one student who “could do better”: students
who come to school without books or homework, students who appear to
choose not to study for exams, students who seem unfazed by parents’ and
teachers’ pleas that their grades now will affect the rest of their professional
lives. We commonly dub these students underachievers.
The research presented in this article is supported under the Educational Research and De
velopment Centers Program, PR/Award No. R206R50001, as administered by the Office of Edu
cational Research and Improvement (OERI), U.S. Department of Education. The opinions ex
pressed in this document do not necessarily reflect the positions or policies of the OERI or the
Department of Education. Correspondence concerning this article should be sent to D. Betsy
McCoach at
Educational and Psychological Measurement, Vol. 63 No. 3, June 2003 414-429
DOI: 10.1177/0013164402251057
© 2003 Sage Publications
Underachievement is most commonly defined as a discrepancy between
potential (or ability) and performance (or achievement) (Reis & McCoach,
2000). Therefore, a student who appears capable of succeeding in school but
is nonetheless struggling is often referred to as an underachiever. Factors
commonly associated with underachievement include low academic self-
concept (Schunk, 1998; Supplee, 1990; Whitmore, 1980), low self-motiva
tion (Weiner, 1992), low goal valuation (McCall, Evahn, & Kratzer, 1992),
and negative attitude toward school and teachers (Colangelo, Kerr,
Christensen, & Maxey, 1993; Ford, 1996; Rimm, 1995). Most of the litera
ture on underachievement suggests that underachievers have lower academic
self-perceptions, lower self-motivation and self-regulation, less goal-
directed behavior, and more negative attitudes toward school than high
achievers do (Reis & McCoach, 2000). However, the majority of research
investigating the common characteristics of underachieving students has
employed qualitative, clinical, or single-subject research methodology. Very
few large-scale quantitative studies have examined the legitimacy of these
hypotheses (Reis & McCoach, 2000). Perhaps so few researchers have exam-
ined the phenomenon of underachievement quantitatively because there are
no adequately validated, published instruments designed specifically for this
The present study represents an attempt to develop an instrument to more
fully probe the issues surrounding the underachievement of able adolescents.
The purpose of this study is to design a psychometrically sound instrument to
measure adolescents’ attitudes toward school, attitudes toward teachers,
goal-valuation, motivation, and general academic self-perceptions that can
be used to explore the underachievement of secondary school students. Our
goal was to design an instrument that can be used to compare heterogeneous
groups of achievers and underachievers as well as achievers and underachiev-
ers from a population of academically gifted students.
The most common definition characterizes underachievement as a dis
crepancy between potential (or ability) and performance (or achievement)
(Dowdall & Colangelo, 1982; Whitmore, 1980). For the purposes of our re
search, we have chosen the following definition of underachievers:
Underachievers are students who exhibit a severe discrepancy between ex
pected achievement [italics added] (as measured by standardized achievement
test scores or cognitive or intellectual ability assessments) and actual achieve
ment [italics added] (as measured by class grades and teacher evaluations). To
be classified as an underachiever, the discrepancy between expected and actual
achievement must not be the direct result of a diagnosed learning disability.
(Reis & McCoach, 2000, p. 157)
Factors Associated With Underachievement
Researchers have attempted to isolate the psychological factors that
appear to be correlated with underachievement. Characteristics commonly
associated with underachievement include low academic self-perceptions,
negative attitude toward school, negative attitudes toward teachers and
classes, low motivation and self-regulation, and low goal valuation (Dowdall &
Colangelo, 1982; Reis & McCoach, 2000; Whitmore, 1980).
Academic self-perceptions. Students develop confidence in many ways,
and those who are confident about their skills are more likely to engage in a
variety of activities. The perceptions students have about their skills influ-
ence the types of activities they select, how much they challenge themselves
at those activities, and the persistence they exhibit once they are involved in
the activities (Ames, 1990; Bandura, 1977, 1986; Schunk, 1981, 1984).
Academic self-concept involves a description and an evaluation of one’s
perceived academic abilities (Byrne, 1996; Hattie, 1992). Academic self-
concept encompasses global beliefs of self-worth associated with one’s per-
ceived academic competence. Academic self-concept is a multidimensional
construct and involves internal and external comparisons. Students compare
their own performance with that of their classmates (an external comparison)
as well as with their own performance in other areas (an internal comparison)
(Byrne, 1996). Social comparison theory suggests that when people compare
favorably to those around them in a particular domain, they are more likely to
maintain high self-concepts in that domain.
Academic self-concept is a significant predictor of academic achievement
(Lyon, 1993; Marsh, Chessor, Craven, & Roche, 1995; Wigfield &
Karpathian, 1991). Research suggests that as much as one third of the vari
ance in achievement can be accounted for by academic self-concept alone
(Lyon, 1993). Furthermore, positive self-concept appears to be linearly
related to subsequent academic achievement (Marsh et al., 1995), although a
flow of causality for this relationship has not been established. Previous liter-
ature suggests that underachievers often exhibit low self-concept or low self-
perceptions (Bruns, 1992; Diaz, 1998; Dowdall & Colangelo, 1982; Ford,
1996; Supplee, 1990; Whitmore, 1980), although some research refutes the
assertion that underachievers have poor academic self-concepts (Holland,
Attitude toward teachers and classes. Teachers’personality and organiza
tion may affect students’ achievement (Peters, Grager-Loidl, & Supplee,
2000). Many underachievers also exhibit problems with authority, including
problems with teachers and school personnel (Mandel & Marcus, 1988;
McCall et al., 1992), and they may exhibit hostility toward authority figures,
including teachers (Mandel & Marcus, 1988). Students’ interest in their
coursework is related to their use of self-regulatory strategies as well as their
motivation (Schiefele, 1991; Wigfield, 1994). Therefore, we expect students’
attitudes toward their teachers and courses, their scores on the motivation/
self-regulation factor, and their academic achievement to exhibit a positive
Attitudes toward school. Attitudes toward school consist of the students’
self-reported interest in and affect toward school. Previous research suggests
that underachievers appear to display negative attitudes toward school
(Bruns, 1992; Diaz, 1998; Ford, 1996; Frankel, 1965; Mandel & Marcus,
1988; McCall et al., 1992; Rimm, 1995). “Research findings over many years
have consistently indicated that young people who do well in school tend to
be interested in learning” (Weiner, 1992, p. 260). Underachievers exhibit
more negative attitudes toward school than average and high achievers do
(Mandel & Marcus, 1988). Majoribanks (1992) found that children’s cogni-
tive attitudes toward school demonstrated moderate, statistically significant
associations with achievement. Interestingly, in his study, affective attitudes
toward school and achievement were correlated for girls but not for boys. As
with academic self-concept, although there appears to be a relationship
between attitude toward school and achievement, this relationship does not
suggest or determine any flow of causality between the two variables.
Goal valuation. Children’s goals and achievement values affect their self-
regulation and motivation (Wigfield, 1994) because goals influence how
children approach, engage in, and respond to achievement tasks (Hidi &
Harackiewicz, 2000). When students value a task, they are more likely to
engage in, expend more effort on, and do better on the task (Wigfield, 1994).
Future goals play an important role in providing task value. Peterson (2000)
followed achieving and underachieving gifted high school students into col
lege. She found the achievers exhibit early decisiveness in their determina
tion of a career direction, and she suggested that these goals may promote
their achievement. Emerick (1992) reported that high school gifted under
achieving students perceived themselves as able to reverse their
underachievement by developing goals that were personally motivating and
directly related to academic success.
Motivation and self-regulation. Recent developments in the field of moti
vation research suggest that self-regulation may hold the key to understand
ing student achievement. Self-regulation refers to students’ “self-generated
thoughts, feelings, and actions which are systematically oriented toward the
attainment of goals” (Zimmerman, 1994, p. ix). Self-regulation comprises
processes by which people are metacognitively, motivationally, and
behaviorally active participants in their own learning (Zimmerman, 1994).
Others suggested that self-regulated learning encompasses three compo
nents: metacognitive strategies, self-management and control of effort, and
cognitive strategy use (Pintrich & DeGroot, 1990). Self-regulation is a signif
icant predictor of academic achievement, and the use of internalized self-reg
ulatory strategies help individuals to achieve in school. However,
“knowledge of cognitive and metacognitive strategies is usually not enough
to promote student achievement; students also must be motivated to use the
strategies as well as regulate their cognition and effort” (Pintrich & De Groot,
1990, p. 33). Unfortunately, disentangling the constructs of motivation and
self-regulation has proven challenging. Underachievers may lack motiva
tion, self-regulation skills, or a combination of the two traits. Underachievers
may not lack knowledge of strategies, but rather they may not understand that
strategic behavior in conjunction with effort results in achievement
(Borkowski & Thorpe, 1994).
The purpose of this study is to validate scores from an instrument designed
to measure academic self-perceptions, attitude toward school, attitudes
toward teachers, goal valuation, and motivation/self-regulation in secondary
school students. The instrument is designed to measure factors that may dis-
tinguish underachievers from achievers in a secondary school setting.
Previous Research on a
Prior Version of the Instrument
The School Attitude Assessment Survey–Revised (SAAS-R) represents
the researchers’ second attempt to quantify factors associated with the scho
lastic achievement and underachievement of adolescents. The previous ver
sion, the School Attitude Assessment Survey (SAAS), measured three of the
five factors contained on the SAAS-R (academic self-perceptions, attitude
toward school, and motivation). In addition, the original SAAS instrument
measured an additional factor, peer attitudes toward school. The SAAS
instrument employed a 7-point Likert-type agreement scale. The pilot ver
sion of the SAAS contained 45 questions and was validated using a conve
nient sample of 1,738 secondary school students from nine different schools
in four states. Twenty of the 45 questions were retained in the final version of
the SAAS. Each of the four factors contained 4 to 6 questions. The scores
from the instrument seemed to exhibit reasonable fit for the hypothesized fac
tor structure in the initial validation sample, χ
(df = 162) = 1,013.4, Tucker
Lewis Index (TLI) = .95, Comparative Fit Index (CFI) = .96, root mean
square error of approximation (RMSEA) = .058, standardized root mean
squared residual (SRMR) = .035 (McCoach, 2002).
When the final, shortened 20-question version of the SAAS was adminis
tered to a new convenient sample of 420 ninth-grade students from a multi
ethnic high school in the South, the factor model fit in the cross-validation
sample was also adequate, χ
(df = 162) = 509.5, p < .001, CFI = .94, TLI =
.92, RMSEA = .075, SRMR = .045 (McCoach, 2002). The scores on the pre
vious version showed internal consistency reliability coefficient of at least
.80 on each of the factors in the initial and cross-validation samples. Further
more, preliminary results suggested that the SAAS could separate approxi
mately 90% of high grade point average (GPA) and low GPA students into the
correct classifications. For more details about the original SAAS instrument,
see McCoach (2002).
We felt that the initial SAAS instrument could be improved for several rea-
sons. First, the motivation/self-regulation and the academic self-perception
factors exhibited a very high correlation of approximately .80. Therefore, we
decided to revise the 20-question version of the SAAS to provide stronger
evidence of discriminant validity among the academic self-perceptions and
motivation/self-regulation factors. In addition, the peer attitudes factor did
not contribute any unique variance to the prediction of students as high or low
achievers after controlling for the other three factors measured by the SAAS.
Therefore, we decided to remove that factor and measure two other factors.
First, we wanted to create a factor to measure students’valuing of the goals of
school. Our belief was that students’valuing the goals of school represented a
necessary prerequisite to their motivation to self-regulate and put forth effort
to achieve in a scholastic environment. Second, we wanted to separate stu-
dents’general attitudes toward school from their attitudes toward their teach
ers and classes. We felt that students might attribute positive affect to the
school for social, athletic, or other reasons without actually having positive
attitudes about their classes and teachers.
Validation Procedures
The goals of content validity are to clarify the domain of a concept and
judge whether the measure adequately represents the domain (Bollen, 1989).
After having conducted a thorough literature review, the researchers created
item stems to measure students’ academic self-perception, attitudes toward
school, attitudes toward teachers and classes, goal valuation, and motivation/
self-regulation. After generating lists of possible item stems, the researchers
selected for inclusion items that met the following criteria: (a) The item
appeared to measure one of the constructs, (b) the item did not appear to
directly or indirectly measure any of the other four constructs, (c) the item
contained only one thought or main idea, and (d) the item was clearly written
so that it could be easily understood by an average fifth grader or a person
with a fifth-grade reading level.
We conducted the construct validation of the SAAS-R using a two-step
process. First, we administered a pilot version of SAAS-R to 942 ninth-
through twelfth-grade students from a mostly middle-class, suburban high
school in the Northeast. The entire school agreed to complete the survey;
therefore, the sample contained variability in terms of their grades, attitudes,
and aspirations. Students completed the surveys anonymously, and the
researchers collected no identifying information. This first version of the
SAAS-R contained 48 questions, each of which was designed to measure one
of the five factors. The SAAS-R employed a 7-point Likert-type agreement
Using EQS 5.7 (Bentler & Wu, 1995), we conducted a confirmatory factor
analysis. We evaluated model fit using several common fit indices including
chi-square (χ
), the ratio of chi-square to degrees of freedom (χ
/d f ),
RMSEA, CFI, TLI (also known as the Bentler-Bonett Non-Normed Fit
Index), and SRMR. We specified a priori that each question was an indicator
for only one factor. Examination of the standardized regression weights (pat-
tern matrix), the standardized residual covariance matrix, the squared multi-
ple correlations of the items, and the modification indices aided in the
respecification of the model. We eliminated one question (“I think that I
would do better in another school”) because it had a factor pattern coefficient
of less than .40 on its specified factor. In addition, after inspecting the modifi-
cation indices, we eliminated 14 questions that appeared to be indicators
of more than one factor, as evidenced by large chi-square change scores
> 40) on the Lagrange multiplier test. We also eliminated one item that
demonstrated several of the largest standardized residual covariances. Using
this process, we eliminated 16 of the original 48 questions. In addition, we
decided to remove two additional questions because we felt that they were
vague and misleading. “I can learn new concepts quickly” was removed from
the original pilot and was rewritten as “I can learn new ideas quickly in
school. “The staff values me as a person” was removed and reworded as “My
teachers care about me.
The final pilot version of the instrument consisted of 30 questions, each of
which being an indicator for one of the five factors. All factor pattern coeffi
cients were significantly different from zero and in the proper direction, and
all factor correlations were significantly different from zero. The final model
exhibited reasonable fit, χ
(395) = 1,577.45, CFI = .93, TLI = .93, RMSEA =
.059, SRMR = .042. Although χ
was significant (p < .001), the χ
cance test is highly sensitive to sample size. The final instrument contained 5
questions on the Academic Self-Perceptions factor, 4 questions on the Atti
tudes Toward Teachers factor, 4 questions on the Attitudes Toward School
factor, 6 questions on the Goal Valuation factor, and 11 questions on the
Motivation/Self-Regulation factor. The confirmatory factor analysis
revealed that the Goal Valuation and Motivation/Self-Regulation factors
were highly correlated (r = .79). The other factors exhibited moderate (.48 to
.66), positive intercorrelations.
Reliability analysis indicated that the scores on the subscales showed
internal consistency reliability coefficient of at least .80 on each of the five
factors (0.82 for the 5-item Academic Self-Perceptions subscale, 0.88 for the
4-item Attitude Toward Teachers subscale, 0.85 for the 4-item Attitude
Toward School subscale, 0.92 for the 6-item Goal Valuation subscale, and
0.94 for the 11-item Motivation/Self-Regulation subscale).
We were concerned by the low numbers of items on the Academic Self-
Perceptions, Attitudes Toward Teachers, and Attitudes Toward School
subscales. Therefore, in addition to rewording the two questions mentioned
above, we wrote 11 new questions. For the Academic Self-Perceptions factor
and the Attitudes Toward Teachers factor, we wrote three new questions and
reworded one question; we wrote three new questions for the Attitudes
Toward School factor. In addition, we wrote one new question each for the
Goal Valuation factor and the Motivation/Self-Regulation factor. This modi-
fied version of the SAAS-R contained the 30 validated items and the 13 new
and newly worded items.
We collected additional data from three different convenience samples.
We sampled a group of rising juniors and seniors in a competitive summer
program for talented high school students on a university campus (n = 146).
For this sample, approximately two thirds of the students were female; one
third were male. Approximately 16% of the sample consisted of African
American students, 9% were Latino American students, and 7% were Asian
American students. In addition, we sampled ninth-grade students attending a
multiethnic urban high school in the Northeast (n = 200). Approximately
57% of the sample was Latino American, and 18% of the sample was African
American. Approximately 56% of these students were female, 40% were
male, and 4% did not indicate their gender on the survey. Finally, we col
lected information from a national sample of academically able achieving
and underachieving high school students from 27 different school districts
nationwide (n = 299). Our sample was 53% male and 47% female. Within the
sample, 35% of the students were seniors, 26% were juniors, 27% were soph
omores, and 10% were freshmen. This subsample of students was predomi
nantly (70%) White; 8% of the sample was African American, and 4% of the
sample was Latino American. Approximately 64% of these students scored
at or above the 95th percentile nationally on a norm-referenced standardized
achievement test.
To examine the psychometric qualities of the revised instrument, we sub
jected the instrument to another confirmatory factor analysis. The sample
size for this analysis was 645 students; however, only the data of 537 com
plete cases were used for the confirmatory factor analysis. In this analysis, we
sequentially eliminated 8 of the 43 items. After eliminating each item, a new
confirmatory factor analysis was conducted. The reasons for elimination of
these eight items were mainly (a) an item showed large standardized residual,
(b) the modification index suggested that an item behaved as an indicator for
more than one factor, (c) content duplication, and (d) item score has very little
variability. Of the eight questions that were eliminated from the instrument,
two (Questions 39 and 43) were newly written questions, and six (Questions
4, 5, 11, 15, 22, and 23) were questions that had appeared on the original pilot
of the SAAS-R.
The final SAAS-R instrument consisted of 35 questions. Of these final 35
questions, 24 were questions from the original pilot version, 2 were reworded
questions, and 9 were newly written questions. Each of the 35 items func
tioned as an indicator of its hypothesized factors. All factor pattern coeffi-
cients were significantly different from zero and in the proper direction, and
all factor correlations were significantly different from zero. The final model
exhibited reasonable fit, χ
(550) = 1,581.7, CFI = .911, TLI = .918, RMSEA =
.059, SRMR = .057. The final instrument contained 8 questions on the Aca-
demic Self-Perceptions factor, 7 questions on the Attitudes Toward Teachers
factor, 5 questions on the Attitudes Toward School factor, 6 questions on the
Goal Valuation factor, and 10 questions on the Motivation/Self-Regulation
factor. Table 1 reports the item stems and structure and pattern coefficients
for the five factors on the final version of the SAAS-R.
Factors 4 (Goal Valuation) and 5 (Motivation/Self-regulation) were still
highly correlated (r = .741), although this correlation was slightly lower than
it had been on the pilot form of the SAAS-R. This high intercorrelation
results in large structure coefficients for Factor 5’s items on Factor 4, as well
as large structure coefficients for Factor 4’s items on Factor 5. The high corre
lation between Goal Valuation and Motivation/Self-Regulation is somewhat
troubling from a discriminant validity point of view; however, it is not unex
pected theoretically. Valuing the goals of school can be seen as a necessary
precursor to putting forth motivation and using self-regulatory strategies to
achieve those goals. Therefore, we believe that students who do not value the
goals of school will be less likely to put forth effort to achieve those goals.
However, students who do value the goals of school may or may not put forth
the necessary effort and use self-regulatory strategies to achieve those goals.
Therefore, we see these two factors as moderately to highly correlated, yet
distinct components of a student’s achievement orientation.
The other factors exhibited moderate (.30 to .65), positive intercorre-
lations. The scores in this study demonstrated a classical theory internal con
Table 1
Factor Pattern and Structure Coefficients for Each of the Five Factors on the Final Version of the School Attitude Assessment Survey–Revised (SAAS-R)
Factor Pattern Factor Structure
2. I am intelligent. .631 .000 .000 .000 .000 .631 .229 .168 .232 .214
3. I can learn new ideas quickly in school. .733 .000 .000 .000 .000 .733 .267 .195 .269 .248
13. School is easy for me. .582 .000 .000 .000 .000 .582 .212 .155 .214 .197
20. I can grasp complex concepts in school. .681 .000 .000 .000 .000 .681 .248 .181 .250 .231
37. I am capable of getting straight As. .628 .000 .000 .000 .000 .628 .229 .167 .230 .213
40. I am good at learning new things in school. .799 .000 .000 .000 .000 .799 .291 .213 .293 .271
41. I am smart in school. .802 .000 .000 .000 .000 .802 .292 .213 .294 .272
1. My classes are interesting. .000 .702 .000 .000 .000 .256 .702 .423 .356 .456
9. I relate well to my teachers. .000 .686 .000 .000 .000 .250 .686 .414 .348 .446
14. I like my teachers. .000 .776 .000 .000 .000 .282 .776 .468 .393 .504
16. My teachers make learning interesting. .000 .839 .000 .000 .000 .305 .839 .506 .425 .545
17. My teachers care about me. .000 .704 .000 .000 .000 .256 .704 .425 .357 .458
31. Most of the teachers at this school are good teachers. .000 .711 .000 .000 .000 .259 .711 .429 .360 .462
34. I like my classes. .000 .788 .000 .000 .000 .287 .788 .475 .400 .512
6. I am glad that I go to this school. .000 .000 .883 .000 .000 .235 .532 .883 .309 .395
7. This is a good school. .000 .000 .858 .000 .000 .228 .517 .858 .294 .384
12. This school is a good match for me. .000 .000 .822 .000 .000 .219 .496 .822 .282 .367
19. I like this school. .000 .000 .575 .000 .000 .153 .347 .575 .197 .257
42. I am proud of this school. .000 .000 .831 .000 .000 .221 .501 .831 .285 .371
Table 1 (conitnued)
Factor Pattern Factor Structure
18. Doing well in school is important for my
future career goals .000 .000 .000 .757 .000 .278 .384 .260 .757 .561
21. Doing well in school is one of my goals. .000 .000 .000 .849 .000 .312 .430 .291 .849 .629
25. It’s important to get good grades in school. .000 .000 .000 .826 .000 .303 .419 .283 .826 .612
28. I want to do my best in school. .000 .000 .000 .558 .000 .205 .283 .191 .558 .413
29. It is important for me to do well in school. .000 .000 .000 .907 .000 .333 .460 .311 .907 .672
38. I want to get good grades in school. .000 .000 .000 .846 .000 .310 .429 .290 .846 .627
8. I work hard at school. .000 .000 .000 .000 .858 .291 .558 .384 .636 .858
10. I am self-motivated to do my schoolwork. .000 .000 .000 .000 .817 .277 .531 .365 .605 .817
24. I complete my schoolwork regularly. .000 .000 .000 .000 .755 .256 .491 .337 .559 .755
26. I am organized about my schoolwork. .000 .000 .000 .000 .756 .256 .491 .338 .560 .756
27. I use a variety of strategies to learn new material. .000 .000 .000 .000 .578 .196 .376 .258 .428 .578
30. I spend a lot of time on my schoolwork. .000 .000 .000 .000 .828 .281 .538 .370 .614 .828
32. I am a responsible student. .000 .000 .000 .000 .407 .138 .265 .182 .302 .407
33. I put a lot of effort into my schoolwork. .000 .000 .000 .000 .886 .300 .576 .396 .657 .886
35. I concentrate on my schoolwork. .000 .000 .000 .000 .888 .301 .577 .397 .658 .888
36. I check my assignments before I turn them in. .000 .000 .000 .000 .707 .240 .460 .316 .524 .707
Note. ASP = academic self-perceptions; ATT = attitudes toward teachers; ATS = attitudes toward school; MOT/S-R = motivation/self-regulation.
sistency reliability coefficient of at least .85 on each of the five factors. Table
2 reports the reliabilities and interfactor correlations of the five factors.
Evidence of Criterion-Related Validity
We hypothesized that this instrument could help distinguish academically
able achievers from academically able underachievers. Therefore, we con-
ducted a series of t tests on the mean scale scores of the five factors to explore
the differences in academically able achievers’ and underachievers’ aca-
demic self-perceptions, attitudes toward teachers, attitudes toward school,
goal valuation, and motivation/self-regulation (see also McCoach & Siegle,
in press). The sample consisted of 176 gifted high school students in Grades 9
through 12 from 28 school districts across the nation. This was a convenient
sample of school district volunteers, and it is not necessarily representative of
high schools nationwide. A contact person at each of the 28 high schools
coordinated the collection of the surveys and student information. The dis
trict contact people used the following definition to identify achieving and
underachieving gifted students in their districts: Gifted achievers were in the
top 10% of their class or had at least a 3.75 GPA. Gifted underachievers were
in the bottom half of their high school class or had a GPA at or below 2.5. Both
groups had an IQ score or achievement score at or above the 92nd percentile.
According to these criteria, the final sample contained 56 gifted underachiev
ers and 120 gifted achievers. Although these definitions are not universally
accepted, they allowed us to examine two distinct groups of students: those
who were, by conventional standards, succeeding in school, and those who
were not achieving at a level commensurate with their expected abilities.
Many of the students in the sample had been identified for gifted programs in
elementary school. The sample consisted of 101 males, 72 females, and 3 stu
dents who did not indicate their gender. Although the gender ratio of male to
Table 2
Cronbach’s Alpha Reliabilities and Interfactor Correlations for the Five Factors of the
School Attitude Assessment Survey–Revised (SAAS-R)
Factor 12345
ASP (.855)
ATT .364 (.892)
ATS .267 .603 (.865)
Goals .367 .507 .343 (.889)
MOT/S-R .339 .65 .447 .741 (.912)
Note. ASP = academic self-perceptions; ATT = attitudes toward teachers; ATS = attitudes toward school;
MOT/S-R = motivation/self-regulation. Reliabilities of the five factors are in parentheses.
female achievers was roughly equal, there were approximately three times as
many male underachievers as there were female underachievers in this sam
ple. This is consistent with previous research on gender differences in
underachievement (Peterson & Colangelo, 1996; Wolfle, 1991). The sample
consisted of 20 freshmen, 50 sophomores, 53 juniors, 50 seniors, and 3 stu
dents who did not indicate their grade level. The majority of the participants
in the study identified themselves as White (78%). In addition, 3% of the par
ticipants were Latino, 2% were African American, 3% were Asian or Pacific
Islander, 12% chose not to respond to the ethnicity question, and 2% self-
reported another ethnicity.
To control the Type I error rate, we used a Bonferroni adjustment, setting
the alpha at .01. To test for equality of variances between the two groups, we
ran a Levene’s test. Only the Attitudes Toward Teachers factor demonstrated
equal variances between the two groups. Therefore, for all other factors, we
utilized Welch’s t test, which adjusts the degrees of freedom to compensate
for the inequality of the variances. For each of the subscales that exhibited
unequal variances, the gifted underachievers displayed greater variances
than the gifted achievers. The mean differences between the achievers and
underachievers’attitudes toward teachers, attitudes toward school, goal valu-
ation, and motivation/self-regulation were statistically significant (p < .001),
and the differences between the gifted achievers and the gifted underachiev-
ers on these four subscales exhibited moderate to large effect sizes (d = .67 to
d = 1.29), with gifted achievers exhibiting higher means than gifted under-
achievers on each of these four subscales. The largest mean differences
between gifted achievers and gifted underachievers occurred on the
Motivation/Self-Regulation subscale and the Goal Valuation subscale; the
effect sizes for both of these mean differences were greater than 1.0. The
mean difference of the Academic Self-Perceptions subscale was not statisti
cally significant, and this was the only subscale that demonstrated a small
effect size (d = .42). Table 3 reports the results of this analysis. The t tests
showed that this instrument could differentiate between gifted achievers and
gifted underachievers. For more details about this study, see also McCoach
and Siegle (in press).
Our findings suggest that the scores on the SAAS-R appear to demonstrate
evidence of adequate construct validity, criterion-related validity, and inter
nal consistency reliability. Therefore, its use as a research instrument seems
justifiable. In addition, four of the five factors of the SAAS-R appear to dif
ferentiate gifted achievers from gifted underachievers (McCoach & Siegle, in
press). It is our hope that the SAAS-R will allow researchers to more fully
understand the relationship between these five factors and underachievement
in gifted and nongifted populations. In addition, we hope this instrument will
provide educators and psychologists a new tool to identify adolescents who
may be at risk for underachievement. Isolating factors that contribute to the
academic underachievement of adolescents is the first step toward reversing
adolescent underachievement. Any instrument that can help educators to
combat these problems merits further exploration and development.
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Table 3
t Tests and Effect Size Measures on Each of the Five Factors on the School Attitude
Assessment Survey–Revised (SAAS-R) Between Gifted High Achievers and Underachievers
Achievers (n = 120) Underachievers (n = 56)
Subscale MSD MSD p d
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ATT 5.33 0.915 4.58 1.015 < .001 0.78
ATS 5.33 1.19 4.41 1.54 .001 0.67
Goal valuation 6.56 0.592 5.32 1.42 < .001 1.23
MOT/S-R 5.39 0.975 3.88 1.37 < .001 1.29
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MOT/S-R = motivation/self-regulation.
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... To extend the nomological network of grit, we examined the relationship between the five subscales of School Attitude Assessment Survey-Revised (SAAS-R; McCoach & Siegle, 2003) and the two grit subscales. The SAAS-R was created to assess beliefs about schooling relevant to gifted students' success. ...
... Pearson Product Moment Correlations Between Grit Scales and Students' School Attitudes for Grade 11 and 12High School Students in the International Baccalaureate Program (n = 617) and Advanced Placement (n = 514) Note. Variables were measured using the School Attitude Assessment Survey-Revised (SAAS-R;McCoach & Siegle, 2003). *p < .05. **p < .01. *** p < .001. ...
... In addition, the 0.619 value obtained for the Nagelkerke R Squared shows that the regression model is well developed. This pseudo-R Squared measure can be treated as somewhat analogous to R Squared in linear regressions ( McCoach and Siegle, 2003 ), and therefore, it can be claimed that 61.9 % of the variance in the model is explained by the predictors. Moreover, the overall classification of 89.8% indicates that the model used is appropriate for this study. ...
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Logistics outsourcing is a practice commonly used by firms to allow them to access capabilities that they lack internally. Although the main drivers of outsourcing in general are fairly well known, the question of what explains logistics outsourcing decisions within the UK pharmaceutical manufacturing industry, in particular, remains under-researched. Therefore, this study aims to bridge the aforementioned gap in the literature. We surveyed 49 drug manufacturers located in the UK using a web-based questionnaire. The data collected were analysed using logistics regression , exploratory factor analysis, and t-tests. We found that UK drug manufacturers regard improving quality and reliability and reducing logistics costs as the most significant reasons for outsourcing logistics services. We also found a direct positive relationship between the service provider's techno-commercial offerings and delivery performance, and the likelihood of being selected to provide these services. We further explored materials transportation, product delivery, research and development, and clinical trials, which are among the most frequently outsourced logistics activities in the UK pharmaceutical manufacturing industry. The study contributes to the wider literature on logistics outsourcing, and more specifically to that on the UK pharmaceutical manufacturing industry. Findings from this research can also be used to guide outsourcing practi-tioners' decisions about the selection of logistics service providers. In addition, the study can help to enhance the service providers' understanding of why firms buy logistics services and which services they are likely to buy.
... Academic self-perception, motivation and self-regulation. The academic self-perception scale consisted of 5 questions measured on a 7-point likert scale (1=strongly disagree to 7=strongly agree) (alpha= 0.89) (McCoach & Siegle, 2003). Questions included 'I am confident in my academic ability' and 'I do well in school'. ...
This exploratory study investigates aspects of sleep quality and some of the potential impacts that sleep complaints for 168 male boarding students from regional and remote communities in Years 7 to 12. An online self-report questionnaire was used to examine the relationship between sleep quality and participants sense of academic self-perception, motivation and regulation, resilience, as well as indicators of non-specific psychological distress, life satisfaction, behavioural and emotional wellbeing. Results found general parity between participants overall scores for sleep quality and norms on the sleep/wake problems scale. However, it emerged that over the previous seven days only a small proportion of participants were satisfied with their sleep every night, with the majority reporting feeling tired or sleepy during the day. This and other findings are discussed in relation to current national sleep recommendations for adolescents, as well as with consideration to the promotion of healthy adolescent development and optimal academic performance, behavioural and cognitive functioning, and emotional regulation. Implications for boarding school routines are discussed with an emphasis on time allocated to sleep, and actual time spent asleep, by adolescent boarders. Strengths and limitations of this study are presented.
... Both raters reported significantly greater inattention problems in the gifted compared to the non-gifted group, with greater frequency in the classroom than at home. The gifted children with higher inattention scores were also those with lower scores in goal-valuation, self-efficacy and, most importantly, self-regulation measured by the School Achievement Attitudes Survey-Revised [72]. Furthermore, they showed lower academic grades. ...
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This work systematically reviewed past literature to investigate the association between intellectual giftedness and socio-emotional and/or behavioral disorders. Nineteen studies met the inclusion criteria, 17 of which have children and/or adolescents as participants, and 12 have a non-gifted control group. Socio-emotional problems, such as withdrawal, were found in 3 out of 8 studies; internalizing disorders, such as anxiety, were found in 5 out of 9; externalizing disorders, such as hyperactivity, were found in 3 out of 5. The most investigated comorbidity was attention-deficit/hyperactivity disorder. A univocal conclusion on the relationship between intellectual giftedness and socio-emotional/behavioral problems cannot be drawn, principally because of the heterogeneity of participants’ age, informants, and instruments. The review highlights the need for future studies to use multi-informant and comprehensive assessments, to reach more robust findings, and suggests that age and discrepancy between verbal and non-verbal intellectual abilities should be considered critical factors.
... Još jedan od razloga za izostajanje organizovane i šire podrške intelektualno izuzetnoj deci možemo tražiti i u nedostatku kompetencija nastavnika i nerazvijenoj metodologiji rada sa darovitim učenicima uopšte. Stavovi prema pružanju posebne obrazovne podrške darovitim učenicima uglavnom su umereno pozitivni (McCoach & Siegle, 2003;Маksić, 2007, kao i prema prethodnoj obuci za rad sa ovom grupom dece (Jung, 2014). ...
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Research goal: The initial premise for this research was that in the population of intellectually above-averagestudents can be potentially giftedness students who need support for the development of their full potential. The aim of this paper was to showwhether the giftedness development of the intellectually above-average students was supported by the teachers in education during the younger school periodand in which form it is. There were analyzed forms of support to students with above-average intelligence thatwere practiced by teachers at the school. The possibility and importance of recognizing the potential giftedness of students by class teachers were discussed in the context of the actualization of intellectual giftedness. Method: The sample consisted of 63 students of both sexes, aged 7.3 to 11 years without neurological deficits, psychiatric disorders, somatic or sensory impairments, and intellectual capacity ranging from 112 to 121 assessed by Raven's Colored Progressive Matrices. Procedures ofsupport and its frequency were determined on the basis of data collected from teachers. Questionnaire for Teacher was used to assess students' academic skills and behavior. Results:By theanalysis of the results was identified two forms of support for intellectually above-averagestudents whichwere practiced in school: the first within extracurricular activities in small separate groups and the second within the classroom. The most common form of support was attending additional classes of advancedextracurricularprogram. This form of support was implemented with47.6% ofstudents. The individual education plan, as a formal form of support, was not developed. Teachers recognize 46.0% of students as academically exceptional and highly dedicated to learning and incontrast 25.5% of those who express only extraordinary IQ. Conclusion: In accordance with the results, there was evident the lack of an additional systemically developed methodology of support to intellectually above-average students at the younger age. This implicite the need to change the educational system according to the one which provides the possibility of supporting and additional interventions in different areas in the early years of education for extraordinary students in the aim of creating an opportunity for developing the potential of giftedness. Key words: above-average intelligence, support in school, giftedness
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The purpose of current study was the effectivness of mindfulness training in reducing the test anxiety and increasing the attitude toward school in 11th grade students in Tehran. The research method is experimental with pretest-posttest design with control group. The statistical population of the study consisted of all students of the 11th grade students of Tehran in the academic year of 2017-2018. From this population, by using a multi-stage clustering method, 88 students (44 girls and 44 boys) who had high scores in Friedben's Anxiety Inventory (1997) and low scores in attitudes toward Schools the McCoach & Siegle Inventory (2003), were selected and randomly assigned to 4 groups (2 experimental and 2 control groups). Then, experimental groups received a Mindfulness protocol in 8 sessions of 2 hours. After completion of the sessions, all subjects were re-evaluated. The data were analyzed by using univariate analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA). The findings of this study showed that mindfulness training is effective in reducing the test anxiety and increasing the attitude toward the school.
The purpose of this study is to suggest the operating case of “Learning-men” as a supporting program for low achieving students of K University in order to objectively verify the effects on both the students’ learning achievement and their core competencies, as well as to provide key implications for the effective operation of a non-subject, educational program for score improvement. Learning-men promotes learning motivation and student success by providing scholarships to excellent students depending on the degree of their academic improvement and by conducting three-step programs for high-risk learners who are likely to accumulate academic warnings due to their repeated low academic achievement. This study analyzed the average change in grade average (GPA) and core competencies before and after the students participated in the program by applying a mixed research method using data from a total of 342 Learning-men students, and from interviews based on 10 academic warnings, academic continuity factors, and program participation effects. Looking at the results of the study, first, both the underachieving students and the self-participating students enrolled in the program who had an academic achievement of ( t =-13.376, p <.001) and core competencies of ( t =-17.867~-21.305, p <.001) improved significantly. Second, 39 out of 45 students who were placed on academic probation (86.7%) improved their grades and were released from their probation. Third, through analyzing the qualitative data, we found that the reason why these students received academic probation was due to university environmental and internal factors, and that their motivation to participate in the program was based on external and internal factors. Nevertheless, their motivation to learn in this program, their academic self-efficacy, and their self-directed learning ability improved. These research results verified that participation in the low academic achievement Support Program can be used as a tool to strengthen college students' academic competencies and core competencies. Based on the results of this study, various systematic operation and effectiveness verification methods for a low academic achievement Support Program were discussed.
This thesis seeks to examine men’s underperformance in schools under Stereotype Threat Theory and through the development a new scale: the Anti-Norm Studying (ANS) scale. The pervading theory, measured qualitatively, behind men’s under achievement is an avoidance of being considered a “study nerd” or in Swedish “plugghäst” which can be literally translated to “study horse”. A quantitative measure, the ANS, was created in order to test this hypothesis. Gender norms in relation to academic self perception, scholastic goals, sexism, and essentialism were also examined. These were correlated with the ANS. Further, academic performance was examined for Stereotype Threat effects after men were primed with the statement“women do better in school than men”. Results showed a significant interaction between gender and condition, indicating stereotype salience for men.
This article describes the development of the School Attitude Assessment Survey (SAAS), an instrument that measures self-concept, self-motivation and self-regulation, attitude toward school, and peer attitudes to predict the academic achievement of adolescents.
Presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from 4 principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. Factors influencing the cognitive processing of efficacy information arise from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. (21/2 p ref)
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.
In 1970 verscheen de eerste editie van dit boek en de lange tussentijd verklaart waarom deze tweede uitgave een grondige bewerking diende te ondergaan. Na een inleidend hoofdstuk over normaliteit/afwijking bij adolescenten volgt een vrij overbodig hoofdstuk over classificatie (de zoveelste DSM–discussie). Hier valt reeds op wat in het hele boek sterk tot uiting komt: dat de auteur een medisch model aankleeft en de psychopathologie van de volwassene telkens als vertrekpunt neemt om vervolgens nog wat over adolescenten te vertellen. In deze zin neemt hij eerst vier grote categorieën tot onderwerp: schizofrenie, affectieve, borderline– en angststoornissen. Waarom een belangrijke groep als de eetstoornissen niet wordt vermeld blijft een raadsel. Dit geldt evenzeer voor seksuele problemen.