Academic Paper Writing Servicehttp://StudyHub.vip/Academic-Performance-Rating-Scale

1. School Psychology Review
Volume 20, No. 2,1991, pp. 284-300
TEACHER RATINGS OF ACADEMIC SKILLS:
THE DEVELOPMENT OF THE
ACADEMIC PERFORMANCE RATING SCALE
George J. DuPaul Mark D. Rapport
University of Massachusetts University of Hawaii
Medical Center at Mama
Lucy M. Perriello
University of Massachusetts Medical Center
Abstract= This study investigated the normative and psychometric properties of
a recently developed teacher checklist, the Academic Pet=fomnance Rating Scale
(APRS), in a large sample of urban elementary school children. This instrument
was developed to assess teacher judgments of academic performance to identify
the presence of academic skills deficits in students with disruptive behavior
disorders and to continuously monitor changes in these skills associated with
treatment. A principal components analysis was conducted wherein a three-factor
solution was found for the APRS. All subscales were found to be internally
consistent, to possess adequate test-retest reliability, and to share variance with
criterion measures of children’s academic achievement, weekly classroom
academic performance, and behavior. The total APRS score and all three subscales
also were found to discriminate between children with and without classroom
behavior problems according to teacher ratings.
The academic performance and ad-
justment of school-aged children has come
under scrutiny over the past decade due
to concerns about increasing rates of
failure and poor standardized test scores
(Children’s Defense Fund, 1988; National
Commission on Excellence in Education,
1983). Reports indicate that relatively
large percentages of children (i.e., 20-30%)
experience academic difficulties during
their elementary school years (Glidewell
& Swallow, 1969; Rubin & Balow, 1978),
and these rates are even higher among
students with disruptive behavior dis-
orders (Cantwell & Satterfield, 1978;
Kazdin, 1986). Further, the results of
available longitudinal studies suggest that
youngsters with disruptive behavior
disorders and concurrent academic per-
formance dficulties are at higher risk for
poor long-term outcome (e.g., Weiss &
Hechtman, 1986).
These fmdings have direct implica-
tions for the assessment of the classroom
functioning of students with behavior
disorders. Specifically, it has become
increasingly important to screen for
possible academic skills deficits in this
population and monitor changes in aca-
demic performance associated with thera-
peutic interventions. Frequently, tradi-
tional measures of academic achievement
(e.g., standardized psychoeducational
batteries) are used as integral parts of the
diagnostic process and for long-term
assessment of academic success. Several
This project was supported in part by BRSG Grant SO7 RR05712 awarded to the first author by the Biomedical
Research Support Grant Program, Division of Research Resources, National Institutes of Health. A portion
of these results was presented at the annual convention of the National Association of School Psychologists,
April, 1990, in San Francisco, CA
The authors extend their appreciation to Craig Edelbrock and three anonymous reviewers for their helpful
comments on an earlier draft of this article and to Russ Barkley, Terri Shelton, Kenneth Fletcher, Gary
Stoner, and the teachers and principals of the Worcester MA Public Schools for their invaluable contributions
to this study.
Address all correspondence to George J. DuPaul, Department of Psychiatry, University of Massachusetts
Medical Center, 55 Lake Avenue North, Worcester, MA 01655.
284

2. Academic Performance Rating Scale 285
factors limit the usefulness of norm-
referenced achievement tests for these
purposes, such as (a) a failure to sample
the curriculum in use adequately, (b) the
use of a limited number of items to sample
various skills, (c) the use of response
formats that do not require the student
to perform the behavior (e.g., writing) of
interest, (d) an insensitivity to small
changes in student performance, and (e)
limited contribution to decisions about
programmatic interventions (Marston,
1989; Shapiro, 1989).
Given the limitations of traditional
achievement tests, more direct measure-
ment methods have been utilized to screen
for academic skills deficits and monitor
intervention effects (Shapiro, 1989; Sha-
piro & Kratochwill, 1988.) Several meth-
ods are available to achieve these pur-
poses, including curriculum-based
measurement (Shinn, 1989), direct obser-
vations of classroom behavior (Shapiro &
Kratochwill, 1988), and calculation of
product completion and accuracy rates
(Rapport, DuPaul, Stoner, & Jones, 1986).
These behavioral assessment techniques
involve direct sampling of academic
behavior and have demonstrated sensitiv-
ity to the presence of skills deficits and
to treatment-induced change in such
performance (Shapiro, 1989).
In addition to these direct assessment
methods, teacher judgments of students’
achievement have been found to be quite
accurate in identifying children in need
of academic support services (Gresham,
Reschly, & Carey, 1987; Hoge, 1983). For
example, Gresham and colleagues (1987)
collected brief ratings from teachers
regarding the academic status of a large
sample of schoolchildren. These ratings
were highly accurate in classifying stu-
dents as learning disabled or non-handi-
capped and were significantly correlated
with student performance on two norm-
referenced aptitude and achievement
tests. In fact, teacher judgments were as
accurate in discriminating between these
two groups as the combination of the
standardized tests.
Although teacher judgments may be
subject to inherent biases (e.g., confirming
previous classification decisions), they
possess several advantages for both
screening and identification purposes.
Teachers are able to observe student
performance on a more comprehensive
sample of academic content than could
be included on a standardized achieve-
ment test. Thus their judgments provide
a more representative sample of the
domain of interest in academic assess-
ment (Gresham et al., 1987). Such judg-
ments also provide unique data regarding
the “teachability” (e.g., ability to succeed
in a regular education classroom) of
students (Gerber & Semmel, 1984). Fi-
nally, obtaining teacher input about a
student’s academic performance can
provide social validity data in support of
classification and treatment-monitoring
decisions. At the present time, however,
teachers typically are not asked for this
information in a systematic fashion, and
when available, such input is considered
to be highly suspect data (Gresham et al.,
1987).
Teacher rating scales are important
components of a multimodal assessment
battery used in the evaluation of the
diagnostic status and effects of treatment
on children with disruptive behavior
disorders (Barkley, 1988; Rapport, 1987).
Given that functioning in a variety of
behavioral domains (e.g., following rules,
academic achievement) across divergent
settings is often affected in children with
such disorders, it is important to include
information from multiple sources across
home and school environments. Unfortu-
nately, most of the available teacher rating
scales specifically target the frequency of
problem behaviors, with few, if any, items
related directly to academic performance.
Thus, the dearth of items targeting teacher
judgments of academic performance is a
major disadvantage of these measures
when screening for skills deficits or mon-
itoring of academic progress is a focus of
the assessment.
To address the exclusivity of the focus
on problem behaviors by most teacher
questionnaires, a small number of rating
scaleshave been developed in recent years
that include items related to academic
acquisition and classroom performance
variables. Among these are the Children’s

3. 286 School Psychology Review, 7997, Vol. 20, No. 2
Behavior Rating &ale (Neeper & Lahey,
1986), Classroom Adjustment Ratings
Scale (Lorion, Cowen, & Caldwell, 1975),
Health Resources Inventory (Gesten,
1976), the Social Skills Rating System
(Gresham & Elliott, 1990), the Teacher-
mild Rating Scale (Hightower et al.,
1986), and the WaZlCimneZZ Scale of
social Chphnceand SchoolAdjustment
(Walker & McConnell, 1988). These scales
have been developed primarily as screen-
ing and problem identification instru-
ments and all have demonstrated relia-
bility and validity for these purposes.
Although all of these questionnaires are
psychometrically sound, each scale pos-
sesses one or more of the following
characteristics that limit its utility for both
screening and progress monitoring of
academic skills deficits. These factors
include (a) items worded at too general
a level (e.g., “Produces work of acceptable
quality given her/his skills level”) to allow
targeting of academic completion and
accuracy rates across subject areas, (b)
a failure to establish validity with respect
to criterion-based measures of academic
success, and (c) requirements for comple-
tion (e.g., large number of items) that
detract from their appeal as instruments
that may be used repeatedly or on a weekly
basis for brief periods.
The need for a brief rating scale that
could be used to identify the presence of
academic skills deficits in students with
disruptive behavior disorders and to
monitor continuously changes in those
skills associated with treatment was
instrumental in the development of the
Academic Performance Rating Scale
(APRS). The APRS was designed to obtain
teacher perceptions of specific aspects
(e.g., completion and accuracy of work in
various subject areas) of a student’s
academic achievement in the context of
a multimodal evaluation paradigm which
would include more direct assessment
techniques (e.g., curriculum-based mea-
surement, behavioral observations). Be-
fore investigating the usefulness of this
measure for the above purposes, its
psychometric properties and technical
adequacy must be established. Thus, this
study describes the initial development of
the APRS and reports on its basic psy-
chometric properties with respect to
factor structure, internal consistency,
test-retest reliability, and criterion-related
validity. In addition, normative data by
gender across elementary school grade
levels were collected.
METHOD
Subjects
Subjects were children enrolled in the
first through sixth grades from 45 public
schools in Worcester, Massachusetts. This
system is an urban, lower middle-class
school district with a 28.5% minority
(African-American, Asian-American, and
Hispanic) population. Complete teacher
ratings were obtained for 493 children
(251 boys and 242 girls), which were
included in factor analytic and normative
data analyses.Children ranged in agefrom
6 to 12 years of age (M = 8.9; SD = 1.8).
A two-factor index of socioeconomic
status (Hollingshead, 1975) was obtained
with the relative percentages of subjects
in each class as follows: I (upper), 12.3%;
II (upper middle), 7.1%; III (middle),
45.5%; IV (lower middle), 26.3% and V
(lower), 8.8%.
A subsample of 50 children, 22 girls
and 28 boys, was randomly selected from
the above sample to participate in a study
of the validity of the APRS. Children at
all grade levels participated, with the
relative distribution of subjects across
grades as follows: first, 19%;second, 16%;
third, 17%; fourth, 17%; fifth, 13.5%; and
sixth, 17.5%. The relative distribution of
subjects across socioeconomic strata was
equivalent to that obtained in the original
sample.
Measures
The primary classroom teacher of
each participant completed two brief
measures: the APRS and Attention/‘h$i-
tit-Hperact+vity Disorder {ADHD] Rat-
ing Scale (DuPaul, in press). In addition,
teachers of the children participating in
the validity study completed the Abbre-
viated Canners Teacher Rating Scale

4. Academic Performance Rating Scale 287
(ACTRS); (Goyette, Conners, & Ulrich,
1978).
APRS. The APRSis a 19-item scalethat
was developed to reflect teachers’ percep-
tions of children’s academic performance
and abilities in classroom settings (see
Appendix A). Thirty items were initially
generated based on suggestions provided
by several classroom teachers, school
psychologists, and clinical child psychol-
ogists. Of the original 30 items, 19 were
retained based on feedback from a sep-
arate group of classroom teachers, prin-
cipals, and school and child psychologists,
regarding item content validity, clarity,
and importance. The final version in-
cluded items directed towards work
performance in various subject areas (e.g.,
“Estimate the percentage of written math
work completed relative to classmates”),
academic success(e.g.,“What is the quality
of this child’s reading skills?“), behavioral
control in academic situations (e.g., “How
often does the child begin written work
prior to understanding the directions?“),
and attention to assignments (e.g., “How
often is the child able to pay attention
without you prompting him/her?“). Two
additional items were included to assess
the frequency of staring episodes and
social withdrawal. Although the latter are
only tangentially related to the afore-
mentioned constructs, they were included
because “overfocused” attention (Kins-
bourne & Swanson, 1979) and reduced
social responding (Whalen, Henker, &
Granger, 1989) are emergent symptoms
associated with psychostimulant treat-
ment. Teachers answered each item using
a 1 (never or poor) to 5 (very often or
excellent) Likert scale format. SevenAPRS
items (i.e., nos. 12,13,15- 19) were reverse-
keyed in scoring so that a higher total
score corresponded with a positive aca-
demic status.
ADHD Rating Scale. The ADHD Rat-
ing Scale consists of 14 items directly
adapted from the ADHD symptom list in
the most recent edition of the Diagnostic
and Statistical Manual of Mental Disorders
(DSM-III-R; American Psychiatric Associ-
ation, 1987). Teachers indicated the
frequency of each symptom on a 1 (not
at all) to 4 (very much) Likert scale with
higher scores indicative of greater ADHD-
related behavior. This scale has been
found to have adequate internal consis-
tency and test-retest reliability, and to
correlate with criterion measures of
classroom performance (DuPaul, in
press).
ACTRS. The ACTRS (or Hyperactivity
Index) is a lo-item rating scale designed
to assessteacher perceptions of psycho-
pathology (e.g., hyperactivity, poor con-
duct, inattention) and is a widely used
index for identifying children at-risk for
ADHD and other disruptive behavior
disorders. It has adequate psychometric
properties and is highly sensitive to the
effects of psychopharmacological inter-
ventions (Barkley, 1988; Rapport, in
press).
Observational measures. Children
participating in the validity study were
observed unobtrusively in their regular
classrooms by a research assistant who
was blind to obtained teacher rating scale
scores. Observations were conducted
during a time when each child was
completing independent seatwork (e.g.,
math worksheet, phonics workbook).
Observations were conducted for 20 min
with on-task behavior recorded for 60
consecutive intervals. Each interval was
divided into 15 s of observation followed
by 5 sfor recording. A child’s behavior was
recorded as on or off-task in the same
manner as employed by Rapport and
colleagues (1982). A child was considered
off-task if (s)he exhibited visual nonatten-
tion to written work or the teacher for
more than 2 consecutive seconds within
each 15 s observation interval, unless the
child was engaged in another task-
appropriate behavior (e.g., sharpening a
pencil). The observer was situated in a
part of the classroom that avoided direct
eye contact with the target child, but at
a distance that allowed easy determina-
tion of on-task behavior. This measure was
included as a partial index of academic
engaged time which has been shown to
be significantly related to academic
achievement (Rosenshine, 1981).

5. 288 School Psychology Review, 7997, Vol. 20, No. 2
Academic efficiency score. Academic
seatwork was assigned by each child’s
classroom teacher at a level consistent
with the teacher’s perceptions of the
child’s ability level with the stipulation
that the assignment be gradeable in terms
of percentage completed and percentage
accurate. Assignments were graded after
the observation period by the research
assistant and teacher, the latter of whom
served as the reliability observer for
academic measures. An academic effi-
ciency score (AES) was calculated in a
manner identical to that employed by
Rapport and colleagues (1986) whereby
the number of items’ completed correctly
by the child was divided by the number
of items assigned to the class multiplied
by 100. This statistic represents the mean
weekly percentage of academic assign-
ments completed correctly relative to
classmates and was used as the class-
room-based criterion measure of aca-
demic performance.
Published norm-referenced achieve-
ment test scores. The results of school-
based norm-referenced achievement tests
(i.e., Comprehensive Test of Basic Skills;
CTB/McGraw-Hill, 1982) were obtained
from the school records of each student
in the validity sample. These tests are
administered routinely on a group basis
in the fall or spring of each school year.
National percentile scores from the most
recent administration (i.e., within the past
year) of this test were recorded for
Mathematics, Reading, and Language
Arts.
Procedure
Regular education teachers from 300
classrooms for grades 1 through 6 were
asked to complete the APRS and ADHD
rating scales with regard to the perfor-
mance of two children in their class.
Teachers from elementary schools in all
parts of the city of Worcester participated
(ie., a return rate of 93.5%) resulting in
a sample that included children from all
socio-economic strata. Teachers were
instructed by one of the authors on which
students to assess (i.e., one boy and girl
randomly selected from class roster), to
complete APRS ratings according to each
child’s academic performance during the
previous week, and that responses on the
ADHD scale were to reflect the child’s
usual behavior over the year. Teacher
ratings for the large sample (N= 487) were
obtained within a l-month period in the
early spring, to ensure familiarity with the
student’s behavior.
A subsample of 50 children was
selected randomly from the larger sample
and parent consent for participation in
the validity study was procured. Teacher
ratings for this subsample were obtained
within a 3-month period in the late winter
and early spring. Teacher ratings on the
APRS were randomly obtained for half of
the sample participating in the validity
study (n = 25) on a second occasion, 2
weeks after the original administration of
this scale, to assesstest-retest reliability.
Ratings reflected children’s academic
performance over the previous week The
research assistant completed the behav-
ioral observations and collected AES data
on 3 separate days (i.e., a total of 60 min
of observation) during the same week that
APRS, ADHD, and ACIRS ratings were
completed. Means (across the 3 observa-
tion days) for percentage on-task and AES
scores were used in the data analyses.
Interobserver reliability. The research
assistant was trained by the first author
to an interobserver reliability of 90% or
greater prior to conducting live observa-
tions using videotapes of children com-
pleting independent work. Reliability
coefficients for on-task percentage were
calculated by dividing agreements by
agreements plus disagreements and mul-
tiplying by 100%.Interobserver reliability
also was assessedweekly throughout the
data collection phase of the study using
videotapes of 10 individual children (who
were participants in the validity study)
completing academic work during one of
the observation sessions. Interobserver
reliability was consistently above 80%with
a mean of 90% for all children. A mean
Kappa coefficient (Cohen, 1960) of .74was
obtained for all observations to indicate
reliability beyond chance levels. Following

6. Academic Performance Rating Scale 289
each observation period, the teacher and
assistant independently calculated the
amount of work completed by the student
relative to classmates and the percentage
of items completed correctly. Interrater
reliability for these measures was consis-
tently above 96% with a mean reliability
of 99%.
Several analyses will be presented to
explicate the psychometric properties of
the APRS.First, the factor structure of this
instrument was determined to aid in the
construction of subscales. Second, the
internal consistency and stability of APRS
scores were examined. Next, gender and
grade comparisons were conducted to
identify the effects these variables may
have on APRSratings aswell as to provide
normative data. Finally, the concurrent
validity of the APRS was evaluated by
calculating correlation coefficients be-
tween rating scale scores and the criterion
measures.
Factor Structure of the APRS
The APRS was factor analyzed using
a principal components analysis followed
by a normalized varimax rotation with
iterations (Bernstein, 1988). As shown in
Table 1, three components with eigen-
values greater than unity were extracted,
accounting for approximately 68% of the
variance: Academic Success (7 items),
Impulse Control (3 items), and Academic
Productivity (12 items). The factor struc-
ture replicated across halved random
subsamples (i.e., n = 242 and 246, respec-
tively). Congruence coefficients (Harman,
1976) between similar components
ranged from 84 to .98 with a mean of .92,
indicating a high degree of similarity in
factor structure across subsamples. Items
with loadings of 60 or greater on a specific
component were retained to keep the
number of complex items (i.e., those with
significant loadings on more than one
factor) to a minimum. In subsequent
analyses, factor (subscale) scores were
calculated in an unweighted fashion with
complex items included on more than one
subscale (e.g., items 3-6 included on both
the Academic Success and Academic
Productivity subscales).
Given that the APRS was designed to
evaluate the unitary construct of aca-
demic performance, it was expected that
the derived factors would be highly
correlated. This hypothesis was confirmed
as the intercorrelations among Academic
Success and Impulse Control, Academic
Success and Academic Productivity, and
Impulse Control and Academic Produc-
tivity were .69, .88, and .63, respectively.
Despite the high degree of overlap between
the Academic Success and Productivity
components (Le., items reflecting accu-
racy and consistency of work correlated
with both), examination of the factor
loadings revealed some important differ-
ences (see Table 1). Specifically, the
Academic Successfactor appears related
to classroom performance outcomes, such
as the quality of a child’s academic
achievement, ability to learn material
quickly, and recall skills. Alternatively, the
Academic Productivity factor is asso-
ciated with behaviors that are important
in the pocess of achieving classroom
success, including completion of work,
following instructions accurately, and
ability to work independently in a timely
fashion.
Internal Consistency and
Reliability of the AIRS
Coefficient alphas were calculated to
determine the internal consistency of the
APRS and its subscales. The results of
these analyses demonstrated adequate
internal consistencies for the Total APRS
(.96), as well as for the Academic Success
(.94) and Academic Productivity (.94)
subscales. The internal consistency of the
Impulse Control subscale was weaker
(.72). Subsequently, the total sample was
randomly subdivided (i.e., n =242 and 246,
respectively) into two independent sub-
samples. Coefficient alphas were calcu-
lated for all APRS scores within each
subsample with results nearly identical to
the above obtained.
Test-retest reliability data were ob-
tained for a subsample of 26 children

7. 290 School Psychology Review, 7997, Vol. 20, No. 2
TABLE1
Factor Structure of the Academic Performance Rating Scale
Scale Item
Academic Impulse
Success Control
Academic
Productivity
I. Math work completed
2. language Arts completed
3. Math work accuracy
4. Language Arts accuracy
5. Consistency of work
6. Follows group instructions
7. Follows small-group instructions
8. Learns material quickly
9. Neatness of handwriting
10. Quality of reading
11. Quality of speaking
12. Careless work completion
13. Time to complete work
14. Attention without prompts
15. Requires assistance
16. Begins work carelessly
17. Recall difficulties
18. Stares excessively
19. Social withdrawal
Estimate of % variance
.30
.32
.60
G
so
rl
.39
.81
z
.87
-80
Iii
.36
.24
.44
.I6
.66
5
.I6
55.5
0.02
.06
.I1
.I7
.21
.35
.37
.I7
.50
,Is
.20
.72
Ti
.35
.39
.82
z
.39
.28
6.6
.84
,82
F3
xi
z
169
,64
36
.31
.23
.21
.36
.61
s3
53
-02
.38
.67
,57
67
Note: Underlined values indicate items included in the factor named in the column head.
(with both genders and all grades repre-
sented) across a 2-week interval as
described previously. The reliability coef-
ficients were uniformly high for the Total
APRS Score (.95), and Academic Success
(.91), Impulse Control (.88), and Aca-
demic Productivity (.93) subscales. Since
rating scale scores can sometimes %n-
prove” simply as a function of repeated
administrations (Barkley, 1988), the two
mean scores for each scalewere compared
using separate t-tests for correlated
measures. Scoresfor each APRSscalewere
found to be equivalent across administra-
tions with t-test results, as follows: Total
APRS Score (t( 24) = 1.24, N.S.), Academic
Success (t( 24) = 1.31, N.S.), Academic
Productivity (t(24) = 1.32, N.S.), and
Impulse Control (t(24) = .15, N.S.).
Gender and Grade Comparisons
Teacher ratings on the APRS were
broken down by gender and grade level
to (a) assessthe effects of these variables
on APRS ratings and (b) provide norma-
tive comparison data. The means and
standard deviations across grade levelsfor
APRS total and subscale scores are
presented for girls and boys in Table 2.
A 2 (Gender) x 6 (Grade) multivariate
analysis of variance (MANOVA) was
conducted employing APRS scores as the
dependent variables. Significant multivar-
iate effects were obtained for the main
effect of Gender (Wilk’s Lambda = .95;fl4,
472) = 6.20, p < .OOl) and the interaction
between Gender and Grade (Wilk’s
Lambda = .93;F(20,1566) = 1.61,~ < .95).
Separate 2 x 6 univariate analyses of

8. Academic Performance Rating Scale 291
TABLE 2
Means and Standard Deviations for the APRS by Grade and Gender
Grade
Total
Score
Academic
Success
Impulse
Control
Academic
Productivity
Grade1 (n =82)
Girls (n = 40)
Boys(n=42)
67.02 (16.27) 23.92 (7.37) 9.76 (2.49) 44.68 (10.91)
71.95 (16.09) 26.86 (6.18) 10.67 (2.82) 46.48 (11.24)
Grade2(n=91)
Girls (n = 46)
Boys(n =45)
Grade 3 (n = 92)
Girls (n = 43)
Boys (n =49)
Grade4(n =79)
72.56
67.84
72.10
68.49
12.33) 26.61 (5.55) 10.15 (2.70) 47.85
14.86) 25.24 (6.15) 9.56 (2.72) 44.30
14.43) 25.07 (6.07 10.86 (2.65) 47.88
16.96) 25.26 (6.53) 9.27 (2.67) 45.61
Girls (n = 38) 67.79 (18.69) 24.08 (7.56) 10.36 (2.91) 44.26
Boys (n=41) 69.77 (15.83) 25.35 (6.50) 9.83 (2.77) 45.71
Grade5(n=79)
Girls (n = 44) 73.02 (14.10) 26.11 (6.01) 10.76 (2.34) 48.36
7.82)
10.76)
9.35)
11.89)
Boys(n =35) 63.68 (18.04) 23.14 (7.31) 8.69 (2.82) 42.40 (12.47)
Grade6(n =70)
Girls (n = 31)
Boys (n =39)
74.10 (14.45) 26.59 (6.26) 10.79 (2.25) 48.77 ( 9.13)
65.24 (12.39) 23.75 (5.90) 9.05 (2.35) 43.59 ( 8.19)
Note: Standard deviations are in parentheses.
variance (ANOVAs) were conducted sub-
sequently for each of the APRS scores to
determine the source of obtained multiv-
ariate effects. A main effect for Gender
was obtained for the APRS Total score
(fll, 476) =6.37,p < .05), Impulse Control
(F(1, 475) = 16.79, p < .OOl), and Aca-
demic Productivity (fll, 475) = 6.95, p <
.05) subscale scores. For each of these
scores, girls obtained higher ratings than
boys, indicating greater teacher-rated
academic productivity and behavioral
functioning among girls. No main effect
for Gender was obtained on Academic
Success subscale scores. Finally, a signif-
icant interaction between Gender and
Grade was obtained for the APRS Total
score (F(5,476) = 2.68,p < .05), Academic
Success (F(5, 475) = 2.63, p < .05), and
Impulse Control (e&475) = 3.59,p < .Ol)
subscale scores. All other main and
interaction effects were nonsignificant.
Simple effects tests were conducted
to elucidate Gender effects within each
Grade level for those variables where a
significant interaction was obtained.
Relatively similar results were obtained
across APRS scores. Gender effects were
found only within grades 6 (fll, 475) =
7.02, p < .Ol) and 6 (fly, 475) = 6.61, p
< .05) for the APRS total score. Alterna-
tively, gender differences on the Academic
Success subscale were obtained solely
within grades 1 (F(1,475) = 4.24, p < .05)
and 5 (F(1, 475) = 4.14, p < .05). These
results indicate that girls in the first and
f&h grades were rated as more academ-
ically competent than boys. Significant
differences between boys and girls in
Impulse Control scores were also found
within grades 3 (fll, 475) = 8.73, p < .Ol),
5 (F(1,475) = 12.24,~ < .OOl), and 6 (F(I,
475) = 8.06, p < .Ol) with girls judged to
exhibit greater behavioral control in these
three grades. All other simple effects tests
were nonsignificant.

9. School Psychology Review, 7997, Vol. 20, No. 2
TABLE 3
Correlations Between APRS Scores and Criterion Measures
Measures
Total Academic
Score Success
Impulse
Control
Academic
Productivity
ACTRS’
ADHD Ratings
On Task Percentage
AES”
CTBS Math
CTBS Reading
CTBS Language
-m6()***b 9.43’” 0.49”” ,.&4***
-.72*** 0.59”’ -.61*** 0.72”“”
.29* .22 .24 .31*
.53*** .26 .41** .57***
.48*** .62*** .28 .39**
.53*** .62*** .34* 44’”
.53*** .61*** .41** .45**
‘Abbreviated Conners Teacher Rating Scale.
bCorrelations are based on N = 50 with degrees of freedom = 48.
‘Academic Efficiency Score.
"pC.05 **p<.o1 -p < .ool
Note: National percentile scores were used for all Comprehensive Test of Basic Skills (CTBS) subscales.
Relationships Among APRS Scores
and Criterion Measures
The relationships among all APRS
scores and several criterion measures
were examined to determine the concur-
rent validity of the APRS. Criterion
measures included two teacher rating
scales(ACTRS, ADHD Rating Scale), direct
observations of on-task behavior, percent-
age of academic assignments completed
correctly @ES), and norm-referenced
achievement test scores (CTBS reading,
math, and language). Pearson product-
moment correlations among these mea-
sures are presented in Table 3. Overall, the
absolute values of obtained correlation
coefficients ranged from .22 to .72 with
24 out of 28 coefficients achieving statis-
tical significance. Further, the APRS Total
Score and Academic Productivity subscale
were found to share greater than 36% of
the variance with the AES, ACTRS, and
ADHD Rating Scale.The Academic Success
subscale shared an average of 38% of the
variance of CTBS scores. Weaker correla-
tions were obtained between APRS scores
and direct observations of on-task behav-
ior with only an average of 7.2% of the
latter’s variance accounted for.
Divergent Validity of the APRS
Correlation coefficients between
APRS scores and criterion measures were
calculated with ACTRS ratings partialled
out to statistically control for variance
attributable to teacher ratings of problem
behavior (see Table 4). Significant rela-
tionships remained between APRS aca-
demic dimensions (i.e., Total Score, Aca-
demic Success, and Academic Pro-
ductivity subscales) and performance
measures such as AES and achievement
test scores. As expected, partialling out
ACTRS scores reduced the correlations
between the Impulse Control subscale and
criterion measures to nonsignificant
levels. None of the partial correlations
with ADHD ratings and on-task percent-
age were statistically significant, indicat-
ing that these criterion measures were
more related to teacher perceptions of a
child’s behavioral control than to his or
her academic performance. The Academic
Success subscale continued to share 26%
or greater of the variance of CTBS scores
when ACIDS scores were partialled out.
In addition, the Total APRS score and the
Academic Productivity subscale shared 9%
of the variance with AES beyond that
accounted for by teacher ratings of
problem behavior.

10. Academic Performance Rating Scale 293
TABLE 4
Correlations Between APRS Scores and Criterion Measures
with ACTRSa Scores Partialled Out
Measures
Total
Score
Academic
Success
Impulse
Control
Academic
Productivity
ADHD Ratings
On Task Percentage
AESC
CTBS Math
CTBS Reading
CTBS Language
-.12b 0.24 0.24 -. 07
0.04 0.01 0.03 9.04
.32* .06 .22 .37**
.38** .56*** .I4 .25
.46*** .58*** .24 .34*
.43** .54*** .28 .30*
*Abbreviated Conners Teacher Rating Scale.
bCorrelations are based on N = 50 with degrees of freedom = 48.
‘Academic Efficiency Score.
*p < .05 *+p < .Ol ““p < a01
Note: National percentile scores were used for all Comprehensive Test of Basic Skills (CTBS) subscales.
The divergent validities of the APRS
subscales were examined to assess the
possible unique associations between
subscale scores and criterion measures.
This was evaluated using separate t-tests
for differences between correlation coef-
ficients that are from the same sample
(Guilford & Fruchter, 1973, p. 167). The
Academic Success subscale was more
strongly associated with CTBS percentile
rankings than the other subscales or
ACTRS ratings. This finding was expected
given that the Academic Success subscale
is comprised of items related to the
outcome of academic performance. Spe-
cifically, the relationship between CTBS
Math scores and Academic Success rat-
ings was significantly greater than that
obtained between CTBS Math scores and
Impulse Control (t(47) = 3.03, p < .Ol),
Academic Productivity (t(47) = 3.11, p <
.Ol, and ACTRS (t(47) = 2.35, p < .05)
ratings. Similar results were obtained for
CTBS Reading scores. The correlation of
the latter with Academic Success ratings
was significantly greater than its relation-
ship with Impulse Control (t(47) = 2.50,
p < .05, Academic Productivity (t(47) =
2.38, p < .05, and ACTRS (t(47) = 2.76,
p < .Ol) ratings. Finally, the relationship
between Academic Success ratings and
CTEB Language scores was significantly
greater than that obtained between the
latter and Academic Productivity ratings
(t(47) = 2.12, p < .OS).
The Academic Productivity subscale
was found to have the strongest relation-
ships with teacher ratings of problem
behavior and accurate completion of
academic assignments. The correlation
between Academic Productivity and
ACTRS ratings was significantly greater
than that obtained between ACTRS and
Academic Success ratings (t(47) = 2.84,
p < .Ol). In a similar fashion, Academic
Productivity ratings were associated to a
greater degree with AES scores than were
Academic Success ratings (t(47) = 4.29,
p < .Ol). Thus, the Academic Productivity
subscale was significantly related to
criterion variables that represent factors
associated with achieving classroom
success(i.e., absenceof problem behaviors
and accurate work completion). It should
be noted that validity coefficients asso-
ciated with the Impulse Control subscales
were not found to be significantly greater
than either of the other subscales.

11. 294 School fsvcholonv Review, 7997,Vol. 20, A/o. 2
, “/
APRS Ratings:
Sensitivity to Group Differences
A final analysis was conducted to
investigate the sensitivity of APRS ratings
to differences between groups of children
with and without attention and impulse
control problems (i.e., the latter group
representing students who are potentially
exhibiting academic performance difficul-
ties). Children from the total sample with
scores 2 standard deviations above the
mean on the ADHD rating scale (n = 35)
were compared with students who re-
ceived teacher ratings of ADHD sympto-
matology within 1 standard deviation of
the mean (n = 390). Separate t-tests were
conducted employing each of the APRS
scores as dependent measures. Statisti-
cally significant differences were obtained
between groups for the APRS Total score
(t( 1,423) = 12.32,~ < .OOl), and Academic
Success (t(1, 423) = 7.23, p < .OOl),
Impulse Control (t( 1, 423) = 8.95, p <
.OOl), and Academic Productivity (t(1,
423) = 10.20,p < .OOl) subscales, with the
children exhibiting ADHD symptoms
rated as significantly inferior on all APRS
dimensions relative to control children.
DISCUSSION
The APRS is a brief teacher question-
naire that provides reliable and valid
information about the quality of a stu-
dent’s academic performance and behav-
ioral conduct in educational situations.
Separate principal components analyses
resulted in the extraction of three com-
ponents or subscales (i.e., Academic
Success, Impulse Control, and Academic
Productivity) that were congruent across
random subsamples. The Academic Suc-
cess subscale accounted for over half of
the variance which supports the construct
validity of the APRS, as it was intended
to assess teacher perceptions of the
quality of students’ academic skills. An
additional 13% of rating variance was
accounted for by the Academic Produc-
tivity and Impulse Control subscales.
Although the latter are highly correlated
with the Academic Successsubscale, both
appear to provide unique information
regarding factors associated with the
process of achieving classroom success
(e.g., work completion, following instruc-
tions, behavioral conduct).
Psychometric Properties of the APRS
The APRS total and subscale scores
were found to possessacceptable internal
consistency, to be stable across a 2-week
interval, and to evidence significant levels
of criterion-related validity. Although the
Impulse Control subscale was found to
have adequate test-retest reliability, its
internal consistency was lower than the
other subscales.This latter finding is likely
due to the fewer number of items in this
subscale. The relationship among APRS
scores and criterion measures, such as
academic efficiency, behavior ratings, and
standardized academic achievement test
scores, were statistically significant. The
APRS Total Score and two subscales were
found to have moderate validity coeffi-
cients and to share appreciable variance
with several subtests of a norm-referenced
achievement test and a measure of
classwork accuracy. Further, when valid-
ity coefficients were calculated with
ACTRS readings partialled out, most
continued to be statistically significant
indicating that APRS scores provide
unique information regarding a child’s
classroom performance relative to brief
ratings of problem behavior.
Two of the three APRS subscaleswere
found to exhibit divergent validity. Al-
though all APRS subscales were positively
correlated with achievement test scores,
the strongest relationships were found
between the Academic Success subscale
and CTBSpercentile rankings, accounting
for an average of 38% of the variance.
Alternatively, although negative correla-
tions were obtained between teacher
report of problem behaviors (i.e., ACTRS
and ADHD ratings) and all APRS scores,
the strongest relationships were found
between the former rating scales and
Academic Productivity scores. Further, a
classroom-based measure of work comple-
tion accuracy (AES) had a significantly
greater correlation with the Academic
Productivity subscale with 32.5%variance

12. Academic Performance Rating Scale 295
accounted for. This latter finding may
appear counterintuitive (i.e., that Aca-
demic Success did not have the strongest
relationship with AES), but is most likely
due to the fact that AES represents a
combination of the child’s academic
ability, attention to task, behavioral
control, and motivation to perform. Given
the varied item content of the Academic
Productivity subscale, it is not surprising
that it shares more variance with a
complex variable like AES. This pattern
of results indicates that the Academic
Successsubscale is most representative of
the teacher’s judgment of a student’s
global achievement status, whereas the
Academic Productivity subscale has a
greater relationship with factors asso-
ciated with the process of day-to-day
academic performance. Finally, although
the Impulse Control subscale was signif-
icantly associated with most of the
criterion measures, it was not found to
demonstrate divergent validity. This
result, combined with its brevity, lower
internal consistency, and redundancy
with teacher ratings of problem behavior,
limits its practical utility as a separate
subscale.
Although statistically significant
positive correlations with on-task percent-
agewere obtained for the APRS Total and
Academic Productivity scores, the Aca-
demic Success and Impulse Control
subscales were not related to this obser-
vational measure. One explanation for this
result is that the Academic Productivity
subscale is more closely related to factors
associated with independent work pro-
ductivity (e.g., attention to task) than are
the other subscales. A second possible
explanation for the weaker correlations
between this criterion variable and all
APRS scores is that children’s classroom
performance is a function of multiple
variables and is unlikely to be represented
by a single, specific construct. As such,
teacher ratings of academic functioning
should be more strongly related to global
measures, such as AES or standardized
achievement test scores, that represent a
composite of ability, attention to task, task
completion and accuracy, than with a
more specific index such as on-task
frequency.
Teacher ratings on the APRS differ-
entiated a group of children displaying
behavior and attention problems from
their normal classmates. Youngsters who
had received scores 2 or more standard
deviations above the mean on a teacher
rating of ADHD symptomatology received
significantly lower scores on all APRS
scales relative to a group of classmates
who were within 1 standard deviation of
the mean on ADHD ratings. This result
provides preliminary evidence of the
APRS’sdiscriminant validity and value for
screening/problem identification pur-
poses. Further studies are necessary to
establish its utility in differentiating
youngsters with disruptive behavior
disorders who are exhibiting concomitant
academic problems versus those who are
not.
APRS: Grade and Gender Differences
Girls were rated to be more compe-
tent than boys on the Academic Produc-
tivity subscale, regardless of grade level.
This result was expected as gender
differences favoring girls have been found
for most similar teacher questionnaires
(e.g.,Weissberg et al., 1987). Alternatively,
for the total and remaining subscale
scores, girls were rated as outperforming
boys only within specific grade levels. In
general, these were obtained at the fifth
and sixth grade levels, wherein gender
differences with respect to achievement
status and behavioral control are most
evident at the upper grades. The latter
result could indicate that gender differ-
ences in daily academic performance do
not impact on teachers’ overall assess-
ment of educational status until the later
grades when demands for independent
work greatly increase. Interestingly, no
significant grade differences were ob-
tained for any of the APRS scores. As
Hightower and colleagues (1986) have
suggested, a lack of differences across
grade levels implies that teachers com-
plete ratings of academic performance in
relative (i.e., in comparison with similar-
aged peers) rather than absolute terms.

13. 296 School Psychology Review, 7997, Vol. 20, No. 2
Limitations of the Present Study
Several factors limit definitive conclu-
sions about the utility of the APRS based
on the present results. First, the sample
of children studied was limited to an
urban location in one geographic region;
it is unknown how representative these
normative data would befor children from
rural or suburban settings aswell as other
regions. Previous research with similar
teacher questionnaires would suggest
significant differences in scores across
urban, suburban, and rural settings (e.g.,
Hightower et al., 1986). Secondly, for the
norms to be generally applicable, APRS
ratings would need to be collected for a
sample representative of the general
population with respect to ethnicity and
socioeconomic status. A further limitation
of the present study was the limited range
of criterion measures employed. In par-
ticular, the relationship of APRS scores
with more direct measures of academic
performance (e.g., criterion-based mea-
surement) should be explored, as the
weaknesses of norm-referenced achieve-
ment tests for this purpose are well
documented (Marston, 1989; Shapiro,
1989). Finally, additional psychometric
properties of this scale, such as predictive
validity and inter-rater reliability, need to
be documented. Empirical investigations
are necessary to determine the usefulness
of the APRS as a treatment-sensitive
instrument. Evidence for the latter is
especially important asa primary purpose
for creating the APRS was to allow
assessment of intervention effects on
academic performance.
Summary
The results of this preliminary inves-
tigation indicate that the APRS is a highly
reliable rating scale that has demon-
strated initial validity for assessing
teacher perceptions of the quality of
student academic performance. Given its
unique focus on academic competencies
rather than behavioral deficits, it appears
to have potential utilitywithin the context
of a multimethod assessment battery. In
particular, it should serve as a valuable
supplement to behavioral assessment
techniques (e.g., direct observations of
behavior, curriculum-based measure-
ment) given its brevity, focus on both
global and specific achievement parame-
ters, and relationship with classroom-
based criteria of academic success. The
present results provide initial support for
the utility of the APRS as a screening/
problem identification measure. Further,
when used in the context of an assessment
battery that includes more direct mea-
sures of academic performance, the APRS
may provide important data regarding the
social validity (i.e., teacher perceptions of
changes in academic status) of obtained
intervention effects, although its incre-
mental validity would need to be estab-
lished. The APRS’ssensitivity to the effects
of behavioral and psychopharmacological
interventions awaits further empirical
study.
American Psychiatric Association. (1987). Diugnos-
tic and statistical manual of mental disorders
(3rd ed. Revised). Washington, DC: Author.
Barkley, R. A. (1988). Child behavior rating scales
and checklists. In M. Rutter, A. H. Tuma, & I. S.
Lann (Eds.), Assessment and diagnosis in child
psychopathology (pp. 113-155). New York:
Guilford.
Bernstein, I. H. (1988). Applied multivariate
analysis. New York: Springer-Verlag.
Cantwell, D. P., & Satterfield, J. H. (1978). The
prevalence of academic under-achievement in
hyperactive children. Journal @‘Pediatric pszlchol-
w, 3, 168-171.
Children’s Defense Fund. (1988). A call for actiun
to make our nation sqfie for children: A briefing
book on the status of American children in 1988.
Washington, DC: Author.
Cohen, J. (1960). A coefficient of agreement for
nominal scales. Educational and pS@&gical
Measurement, 20,37-46.
CTB/McGraw-Hill. (1982). l%e comprehensive Test
of Basic Skills. Monterey, CA Author.
DuPaul, G. J. (in press). Parent and teacher ratings
of ADHD symptoms: Psychometric properties in a
community-based sample. Journal of Clinical
Child Psychologg.

14. Academic Performance Rating Scale 297
Gerber, M. M., & Semmel, M. I. (1984). Teacher as
imperfect test: Reconceptualizing the referral
process. Educational Psychologist, 19, 137-148.
Gesten, E. L. (1976). A Health Resources Inventory:
The development of a measure of the personal and
social competence of primary-grade children.
Journal of Consulting and Clinical Psychology, 4-4,
775-786.
Glidewell, .I. C., & Swallow, C. S. (1969). The
prevalence of maladjustment in elementary
schools. Report prepared for the Joint Commission
on Mental Illness and Health of Children. Chicago:
University of Chicago Press.
Goyette, C. H., Conners, C. K., & Ulrich, R. F. (1978).
Normative data on Revised Conners Parent and
Teacher Rating Scales. Journal ofAbnormal Child
Psychdogy, 6,221-236.
Gresham, F. M., & Elliott, S. N. (1990). Social skills
rating system. Circle Pines, MN: American Guid-
ance Service.
Gresham, F. M., Reschly, D. .I., & Carey, M. P. (1987).
Teachers as “tests”: Classification accuracy and
concurrent validation in the identification of
learning disabled children. S&ool Psychology
Rewiim, 16,543-553.
Guilford, .I. P., & Fruchter, B. (1973). Fundamental
statistics in psychology and education (5th ed.).
New York: McGraw-Hill.
Harman, H. H. (1976). Malern factor analysis (3rd
ed.-revised). Chicago: The University of Chicago
Press.
Hightower, A. D., Work, W. C., Cowen, E. L.,
Lotyczewski, B. S., Spine& A. T., Guare, J. C., &
Rohrbeck, C. A. (1986). The Child Rating Scale: The
development of a socioemotional self-rating scale
for elementary school children. school Psychology
Review, 16,239-255.
Hoge, R. D. (1983). Psychometric properties of
teacher-judgment measures of pupil aptitudes,
classroom behaviors, and achievement levels.
Journal of &x&al Education, 17,401-429.
Hollingshead, A. B. (1975). Fourfactor index of social
status. New Haven, CT Yale University, Department
of Sociology.
Kazdin, A. E. (1985). Treatment of antisocial
behavior in children and adolescents. Homewood,
IL: Dorsey Press.
Kinsbourne, M., & Swanson, J. M. (1979). Models of
hyperactivity: Implications for diagnosis and
treatment. In R. L. Trites (Ed.), Hyperactivity in
children: Etiology, measurement, and treatment
implications (pp. l-20). Baltimore: University
Park Press.
Lorion, R. P., Cowen, E. L., & Caldwell, R. A. ( 1975).
Normative and parametric analyses of school
maladjustment. American Journal of Community
Psychology, 3,291-301.
Marston, D. B. (1989). A curriculum-based measure-
ment approach to assessing academic perfor-
mance: What it is and why do it. In M. R. Shinn
(Ed.), Curriculum-based measurement: Assessing
special children (pp. 18-78). New York: Guilford
Press.
National Commission on Excellence in Education.
(1983). A nation at risk: 17Le immative for
educational reform. Washington, DC: Author.
Neeper, R., & Lahey, B. B. (1986). The Children’s
Behavior Rating Scale: A factor analytic develop-
mental study. school Psychology Reuiew, 15, 277-
288.
Rapport, M. D. (1987). Attention Deficit Disorder
with Hyperactivity. In M. Hersen &V. B. Van Hasselt
(Eds.), Behavior therapy with children and
adolescents (pp. 325-361). New York: Wiley.
Rapport, M. D. (in press). Psychostimulant effects
on learning and cognitive function in children with
Attention Deficit Hyperactivity Disorder: Findings
and implications. In J. L. Matson (Ed.), Hwac-
tivity in children: A handbook. New York:
Pergamon Press.
Rapport, M. D., DuPaul, G. J., Stoner, G., & Jones,
J. T. (1986). Comparing classroom and clinic
measures of attention deficit disorder: Differential
idiosyncratic, and dose-response effects of methyl-
phenidate. Journal of Consulting and Clinical
PsycWQgy, 54,334-341.
Rapport, M. D., Murphy, A., & Bailey, J. S. (1982).
Ritalin vs. response cost in the control of hyper-
activity children: A within-subject comparison.
Journal of Applied Behavior Analysis, 15, 205-
216.
Rosenshine, B. V. (1981). Academic engaged time,
content covered, and direct instruction. Journal
of Education, 3,38-66.
Rubin, R. A, & Balow, B. (1978). Prevalence of
teacher-identified behavior problems. Exceptional
Children, 45, 102-111.
Shapiro, E. S. (1989). Academic skills problems:
Direct assessment and intervention. New York:
GuiIford Press.
Shapiro, E. S., & Kratochwill, T. R. (Eds.). (1988).
Behavioral assessment in schools: Conceptual
foundations and practical applications. New York:
Guilford Press.
Shinn, M. R. (Ed.). (1989). Curriculum-based
measurement: Assessing special children. New
York: Guilford Press.

15. 290 School Psychology Review, 7997, Vol. 20, No. 2
Wallrer,H. M., & McConnell, S. R. (1988). ?ViuZti- & Gesten,E.L (1987).Teacherratings ofchildren’s
M&ml1 &ale of social GmqMmce and &hool problem and competence behatiors: Normative
A- Austin, TX: Pro-Ed,Inc. and parametric characteristics.AmericanJoumMtl
Weiss, G., & Hechtman, L. (1986). Hyperactive
c#cOmmun~pszlcho~, 15,387-401.
clddm grown up. New York:GuMord. Whalen,C. K., Henker,B., & Granger,D. A. (1989).
Weissberg,
R.P.,Cowen,E.L.,Lotyczewski,B.S.,Boike,
Ratings of medication effects in hyperactive
children: Viableor vulnerable?Behavioral Assess-
M. F., Orara, N., Ahvay, Stalonas, P., Sterling, S., ment, 11,179.199.
-7 e J.DuPauI, PhD, received his doctoratefrom the University of Rhode
lslan in 1985. He is currently Assistant Professor of Psychiatry at the
University of Massachusetts Medical Center. His research interests include
the assessment and treatment of Attention Deficit Hyperactivity Disorder
and related behavior disorders.
Mak D. Rapport, PhD, is currently Associate Professor of Psychology at
the University of Hawaii at Manoa. His research interests include assessment
of the cognitive effects of psychotropic medications and the treatment of
Attention Deficit Hyperactivity Disorder and related behavior disorders.
Lucy M. PerrieIIo, MA, received a Master’s degree in Counseling Psychology
from Assumption College in 1988. She is currently a Research Associate
in Behavioral Medicine at the University of Massachusetts Medical Center.

16. Academic Performance Rating Scale
APPENDIX A
Student Date
Grade Teacher
For each of the below items, please estimate the above student’s performance over the PAST
WEEK. For each item, please circle one choice only.
Estimate the percentage of
written math work completed
(regardless of accuracy) rela-
tive to classmates.
Estimate the percentage of
written language arts work
completed (regardless of ac-
curacy) relative to classmates.
.Estimate the accuracy of com-
4 pleted written math work
(i.e., percent correct of work
done).
4. Estimate the accuracy of com-
pleted written language arts
work (i.e., percent correct of
work done).
5. How consistent has the qual-
ity of this child’s academic
work been over the past
week?
6. How frequently does the stu-
dent accurately follow teacher
instructions and/or class dis-
cussion during large-group
(e.g., whole class) instruction?
7. How frequently does the stu-
dent accurately follow teacher
instructions and/or class dis-
cussion during small-group
(e.g., reading group)
instruction?
8. How quickly does this child
learn new material (i.e., pick
up novel concepts)?
9. What is the quality or neat-
ness of this child’s
handwriting?
049% 5049% 70-79% 8049% 90-100%
1 2 3 4 5
049% 5049% 70-79% 804% 90400%
I 2 3 4 5
044% 65-69% 70-79% 8049% 90-100%
1 2 3 4 5
044% 6549% 70-79% 8&89% 90400%
1 2 3 4 5
Consistently More Poor Variable More Consistently
Poor than Successful successful
Successful than Poor
1 2 3 4 5
Never Rarely Sometimes Often Very often
1 2 3 4 5
Never Rarely Sometimes Often Very often
1 2 3 4 5
Very Slow Slow Average Quickly very
Quickly
1 2 3 4 5
Poor Fair Average Above Excellent
Average
I 2 3 4 5

17. 300 SchoolPsychologyReview,7997, Vo/.2OJVo.2
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
What is the quality of this
child’s reading skills?
What is the quality of this
child’s speaking skills?
How often does the child
complete written work in a
careless, hasty fashion?
How frequently does the
child take more time to com-
plete work than his/her
classmates?
How often is the child able to
pay attention without you
prompting him/her?
How frequently does this
child require your assistance
to accurately complete his/
her academic work?
How often does the child
begin written work prior to
understanding the directions?
How frequently does this
child have difficulty recalling
material from a previous day’s
lessons?
How often does the child ap-
pear to be staring excessively
or “spaced out”?
How often does the child ap-
pear withdrawn or tend to
lack an emotional response in
a social situation?
Poor Fair Average Above Excellent
Average
1 2 3 4 5
Poor Fair Average Above
Average
Excellent
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5
Never Rarely Sometimes Often Very Often
1 2 3 4 5