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
Medical Center
University of Hawaii
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 adjustment 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 disorders (Cantwell & Satterfield, 1978;
Kazdin, 1986). Further, the results of
available longitudinal studies suggest that
youngsters with disruptive behavior
disorders and concurrent academic performance dficulties are at higher risk for
poor long-term outcome (e.g., Weiss &
Hechtman, 1986).
These fmdings have direct implications 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 academic performance associated with therapeutic interventions. Frequently, traditional 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
Medical Center, 55 Lake Avenue North, Worcester, MA 01655.
284
of Psychiatry,
University
of Massachusetts
Academic
Performance
Rating Scale
285
factors limit the usefulness of norm- possess several advantages for both
referenced achievement tests for these screening and identification purposes.
purposes, such as (a) a failure to sample Teachers are able to observe student
the curriculum in use adequately, (b) the performance on a more comprehensive
use of a limited number of items to sample sample of academic content than could
various skills, (c) the use of response be included on a standardized achieveformats that do not require the student ment test. Thus their judgments provide
to perform the behavior (e.g., writing) of a more representative sample of the
interest, (d) an insensitivity to small domain of interest in academic assesschanges in student performance, and (e) ment (Gresham et al., 1987). Such judglimited contribution to decisions about ments also provide unique data regarding
the “teachability” (e.g., ability to succeed
programmatic interventions (Marston,
in a regular education classroom) of
1989; Shapiro, 1989).
students (Gerber & Semmel, 1984). FiGiven the limitations of traditional
achievement tests, more direct measure- nally, obtaining teacher input about a
ment methods have been utilized to screen student’s academic performance can
for academic skills deficits and monitor provide social validity data in support of
intervention effects (Shapiro, 1989; Sha- classification and treatment-monitoring
piro & Kratochwill, 1988.) Several meth- decisions. At the present time, however,
ods are available to achieve these pur- teachers typically are not asked for this
information in a systematic fashion, and
poses, including
curriculum-based
measurement (Shinn, 1989), direct obser- when available, such input is considered
vations of classroom behavior (Shapiro & to be highly suspect data (Gresham et al.,
Kratochwill, 1988), and calculation of 1987).
Teacher rating scales are important
product completion and accuracy rates
(Rapport, DuPaul, Stoner, & Jones, 1986). components of a multimodal assessment
These behavioral assessment techniques battery used in the evaluation of the
diagnostic status and effects of treatment
involve direct sampling of academic
behavior and have demonstrated sensitiv- on children with disruptive behavior
ity to the presence of skills deficits and disorders (Barkley, 1988; Rapport, 1987).
to treatment-induced
change in such Given that functioning in a variety of
behavioral domains (e.g., following rules,
performance (Shapiro, 1989).
In addition to these direct assessment academic achievement) across divergent
methods, teacher judgments of students’ settings is often affected in children with
achievement have been found to be quite such disorders, it is important to include
accurate in identifying children in need information from multiple sources across
of academic support services (Gresham, home and school environments. UnfortuReschly, & Carey, 1987; Hoge, 1983). For nately, most of the available teacher rating
example, Gresham and colleagues (1987) scales specifically target the frequency of
collected brief ratings from teachers problem behaviors, with few, if any, items
regarding the academic status of a large related directly to academic performance.
sample of schoolchildren. These ratings Thus, the dearth of items targeting teacher
were highly accurate in classifying stu- judgments of academic performance is a
major disadvantage of these measures
dents as learning disabled or non-handiwhen screening for skills deficits or moncapped and were significantly correlated
with student performance on two norm- itoring of academic progress is a focus of
the assessment.
referenced aptitude and achievement
To address the exclusivity of the focus
tests. In fact, teacher judgments were as
accurate in discriminating between these on problem behaviors by most teacher
two groups as the combination of the questionnaires, a small number of rating
scales have been developed in recent years
standardized tests.
Although teacher judgments may be that include items related to academic
acquisition and classroom performance
subject to inherent biases (e.g., confirming
previous classification decisions), they variables. Among these are the Children’s
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 Teachermild Rating Scale (Hightower et al.,
1986), and the WaZlCimneZZ
Scale of
social Chphnceand
SchoolAdjustment
(Walker & McConnell, 1988). These scales
have been developed primarily as screening and problem identification instruments and all have demonstrated reliability and validity for these purposes.
Although all of these questionnaires are
psychometrically sound, each scale possesses 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 completion (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 measurement, behavioral observations). Before 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 psychometric 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 age from
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$itit-Hperact+vity
Disorder {ADHD] Rating Scale (DuPaul, in press). In addition,
teachers of the children participating in
the validity study completed the Abbreviated Canners Teacher Rating Scale
Academic
Performance
(ACTRS); (Goyette, Conners, & Ulrich,
1978).
APRS. The APRS is a 19-item scale that
was developed to reflect teachers’ perceptions 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 psychologists. Of the original 30 items, 19 were
retained based on feedback from a separate group of classroom teachers, principals, and school and child psychologists,
regarding item content validity, clarity,
and importance. The final version included 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 aforementioned constructs, they were included
because “overfocused” attention (Kinsbourne & Swanson, 1979) and reduced
social responding (Whalen, Henker, &
Granger, 1989) are emergent symptoms
associated with psychostimulant treatment. 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 reversekeyed in scoring so that a higher total
score corresponded with a positive academic status.
ADHD Rating Scale. The ADHD Rating 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 Association, 1987). Teachers indicated the
frequency of each symptom on a 1 (not
Rating Scale
287
at all) to 4 (very much) Likert scale with
higher scores indicative of greater ADHDrelated behavior. This scale has been
found to have adequate internal consistency 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 assess teacher perceptions of psychopathology (e.g., hyperactivity, poor conduct, 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 interventions (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 s for 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 nonattention 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 taskappropriate 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 determination 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).
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 efficiency 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 assignments completed correctly relative to
classmates and was used as the classroom-based criterion measure of academic performance.
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 assess test-retest reliability.
Ratings reflected children’s academic
performance over the previous week The
research
assistant completed the behavPublished norm-referenced
achieveioral
observations
and collected AES data
ment test scores. The results of schoolon
3
separate
days
(i.e., a total of 60 min
based norm-referenced achievement tests
of
observation)
during
the same week that
(i.e., Comprehensive Test of Basic Skills;
APRS,
ADHD,
and
ACIRS
ratings were
CTB/McGraw-Hill, 1982) were obtained
completed.
Means
(across
the
3 observafrom the school records of each student
tion
days)
for
percentage
on-task
and AES
in the validity sample. These tests are
scores
were
used
in
the
data
analyses.
administered routinely on a group basis
Interobserver reliability. The research
in the fall or spring of each school year.
National percentile scores from the most assistant was trained by the first author
recent administration (i.e., within the past to an interobserver reliability of 90% or
year) of this test were recorded for greater prior to conducting live observaMathematics, Reading, and Language tions using videotapes of children completing independent work. Reliability
Arts.
coefficients for on-task percentage were
calculated by dividing agreements by
Procedure
agreements plus disagreements and mulRegular education teachers from 300 tiplying by 100%. Interobserver reliability
classrooms for grades 1 through 6 were also was assessed weekly throughout the
asked to complete the APRS and ADHD data collection phase of the study using
rating scales with regard to the perfor- videotapes of 10 individual children (who
mance of two children in their class. were participants in the validity study)
Teachers from elementary schools in all completing academic work during one of
parts of the city of Worcester participated
the observation sessions. Interobserver
(ie., a return rate of 93.5%) resulting in reliability was consistently above 80%with
a sample that included children from all a mean of 90% for all children. A mean
socio-economic strata. Teachers were Kappa coefficient (Cohen, 1960) of .74 was
instructed by one of the authors on which obtained for all observations to indicate
students to assess (i.e., one boy and girl reliability beyond chance levels. Following
Academic Performance Rating Scale
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 consistently above 96% with a mean reliability
of 99%.
289
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 academic 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
Several analyses will be presented to Impulse Control and Academic Producexplicate the psychometric properties of tivity were .69, .88, and .63, respectively.
the APRS. First, the factor structure of this Despite the high degree of overlap between
instrument was determined to aid in the the Academic Success and Productivity
construction of subscales. Second, the components (Le., items reflecting accuinternal consistency and stability of APRS racy and consistency of work correlated
scores were examined. Next, gender and with both), examination of the factor
grade comparisons were conducted to loadings revealed some important differidentify the effects these variables may ences (see Table 1). Specifically, the
have on APRS ratings as well as to provide Academic Success factor appears related
normative data. Finally, the concurrent
to classroom performance outcomes, such
validity of the APRS was evaluated by as the quality of a child’s academic
calculating correlation coefficients be- achievement, ability to learn material
tween rating scale scores and the criterion quickly, and recall skills. Alternatively, the
measures.
Academic Productivity factor is associated with behaviors that are important
in the pocess of achieving classroom
Factor Structure of the APRS
success, including completion of work,
The APRS was factor analyzed using following instructions accurately, and
a principal components analysis followed ability to work independently in a timely
by a normalized varimax rotation with fashion.
iterations (Bernstein, 1988). As shown in
Table 1, three components with eigen- Internal Consistency and
values greater than unity were extracted,
accounting for approximately 68% of the Reliability of the AIRS
Coefficient alphas were calculated to
variance: Academic Success (7 items),
Impulse Control (3 items), and Academic determine the internal consistency of the
Productivity (12 items). The factor struc- APRS and its subscales. The results of
ture replicated across halved random these analyses demonstrated adequate
subsamples (i.e., n = 242 and 246, respec- internal consistencies for the Total APRS
tively). Congruence coefficients (Harman, (.96), as well as for the Academic Success
(.94) and Academic Productivity (.94)
1976) between similar components
ranged from 84 to .98 with a mean of .92, subscales. The internal consistency of the
indicating a high degree of similarity in Impulse Control subscale was weaker
factor structure across subsamples. Items (.72). Subsequently, the total sample was
with loadings of 60 or greater on a specific randomly subdivided (i.e., n = 242 and 246,
component were retained to keep the respectively) into two independent subnumber of complex items (i.e., those with samples. Coefficient alphas were calcusignificant loadings on more than one lated for all APRS scores within each
factor) to a minimum. In subsequent subsample with results nearly identical to
analyses, factor (subscale) scores were the above obtained.
Test-retest reliability data were obcalculated in an unweighted fashion with
complex items included on more than one tained for a subsample of 26 children
290
School Psychology
Factor Structure
Review, 7997, Vol. 20, No. 2
TABLE1
of the Academic
Performance
Academic
Success
Scale Item
Rating
Scale
I. Math work completed
2. language
Arts completed
.30
0.02
.32
3. Math
.60
.06
.I1
work
4. Language
accuracy
of work
6. Follows
group
instructions
7. Follows
small-group
8. Learns material
9. Neatness
10. Quality
11. Quality
instructions
quickly
.39
.37
.81
.I7
.50
.31
z
of handwriting
.87
-80
of reading
of speaking
12. Careless work completion
13. Time to complete
work
Iii
.36
14. Attention
15. Requires
.24
.44
without
prompts
assistance
16. Begins work
carelessly
55.5
values indicate items included
.36
Ti
.61
s3
53
-02
.35
.39
.38
.67
,57
.28
6.6
in the factor named in the column
(with both genders and all grades represented) across a 2-week interval as
described previously. The reliability coefficients were uniformly high for the Total
APRS Score (.95), and Academic Success
(.91), Impulse Control (.88), and Academic Productivity (.93) subscales. Since
rating scale scores can sometimes %nprove” simply as a function of repeated
administrations (Barkley, 1988), the two
mean scores for each scale were compared
using separate t-tests for correlated
measures. Scores for each APRS scale were
found to be equivalent across administrations 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.).
.23
.21
.20
.72
.82
.I6
Underlined
,Is
z
.39
19. Social withdrawal
Note:
.21
.35
.66
5
of % variance
.I7
.I6
17. Recall difficulties
18. Stares excessively
Estimate
.84
,82
F3
xi
z
169
,64
36
G
so
rl
Arts accuracy
5. Consistency
Academic
Productivity
Impulse
Control
67
head.
Gender and Grade Comparisons
Teacher ratings on the APRS were
broken down by gender and grade level
to (a) assess the effects of these variables
on APRS ratings and (b) provide normative comparison data. The means and
standard deviations across grade levels for
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 multivariate 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
Academic
Means
and Standard
Deviations
Total
Score
Grade
Performance
291
Rating Scale
TABLE 2
for the APRS by Grade and Gender
Academic
Success
Impulse
Control
Academic
Productivity
Grade1
(n =82)
Girls (n = 40)
Boys(n=42)
67.02 (16.27)
71.95 (16.09)
23.92 (7.37)
26.86 (6.18)
9.76 (2.49)
10.67 (2.82)
44.68 (10.91)
46.48 (11.24)
Grade2(n=91)
Girls (n = 46)
Boys(n =45)
72.56
67.84
12.33)
14.86)
26.61 (5.55)
25.24 (6.15)
10.15 (2.70)
9.56 (2.72)
47.85
44.30
7.82)
10.76)
Grade 3 (n = 92)
Girls (n = 43)
Boys (n =49)
72.10
68.49
14.43)
16.96)
25.07 (6.07
25.26 (6.53)
10.86 (2.65)
9.27 (2.67)
47.88
45.61
9.35)
11.89)
Grade4(n
=79)
Girls (n = 38)
Boys (n=41)
67.79 (18.69)
69.77 (15.83)
24.08 (7.56)
25.35 (6.50)
10.36 (2.91)
9.83 (2.77)
44.26
45.71
Grade5(n=79)
Girls (n = 44)
Boys(n =35)
73.02 (14.10)
63.68 (18.04)
26.11 (6.01)
23.14 (7.31)
10.76 (2.34)
8.69 (2.82)
48.36
42.40 (12.47)
Grade6(n
=70)
Girls (n = 31)
Boys (n =39)
74.10 (14.45)
65.24 (12.39)
26.59 (6.26)
23.75 (5.90)
10.79 (2.25)
9.05 (2.35)
48.77 ( 9.13)
43.59 ( 8.19)
Note:
Standard deviations
are in parentheses.
variance (ANOVAs) were conducted subsequently for each of the APRS scores to
determine the source of obtained multivariate 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 Academic 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 significant 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. Alternatively, 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 academically 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.
School Psychology
Correlations
TABLE 3
APRS Scores
Total
Score
Measures
ACTRS’
ADHD
Between
Ratings
Review, 7997, Vol. 20, No. 2
and Criterion
Measures
Academic
Success
Impulse
Control
Academic
Productivity
-m6()***b
9.43’”
0.49””
,.&4***
-.72***
0.59”’
-.61***
0.72”“”
On Task Percentage
.29*
.22
.24
.31*
AES”
.53***
.26
.41**
.57***
CTBS Math
.48***
.62***
.28
.39**
CTBS Reading
.53***
.62***
.34*
44’”
CTBS Language
.53***
.61***
.41**
.45**
‘Abbreviated
Conners
Teacher Rating Scale.
bCorrelations are based on N = 50 with degrees of freedom
‘Academic Efficiency Score.
**p<.o1
"pC.05
Note:
National
Relationships
and Criterion
percentile
-p
= 48.
< .ool
scores were used for all Comprehensive
Among APRS Scores
Measures
Divergent
Test of Basic Skills (CTBS) subscales.
Validity of the APRS
Correlation coefficients between
APRS
scores and criterion measures were
The relationships among all APRS
calculated
with ACTRS ratings partialled
scores and several criterion measures
out
to
statistically
control for variance
were examined to determine the concur- attributable to teacher
ratings of problem
rent validity of the APRS. Criterion
behavior (see Table 4). Significant relameasures included two teacher rating tionships remained between APRS acascales (ACTRS, ADHD Rating Scale), direct demic dimensions (i.e., Total Score, Acaobservations of on-task behavior, percent- demic Success, and Academic Proage of academic assignments completed ductivity subscales) and performance
correctly @ES), and norm-referenced
measures such as AES and achievement
achievement test scores (CTBS reading, test scores. As expected, partialling out
math, and language). Pearson productACTRS scores reduced the correlations
moment correlations among these mea- between the Impulse Control subscale and
sures are presented in Table 3. Overall, the criterion measures to nonsignificant
levels. None of the partial correlations
absolute values of obtained correlation
coefficients ranged from .22 to .72 with with ADHD ratings and on-task percent24 out of 28 coefficients achieving statis- age were statistically significant, indicattical significance. Further, the APRS Total ing that these criterion measures were
Score and Academic Productivity subscale more related to teacher perceptions of a
child’s behavioral control than to his or
were found to share greater than 36% of her
academic performance. The Academic
the variance with the AES, ACTRS, and Success subscale continued to share 26%
ADHD Rating Scale.The Academic Success or greater of the variance of CTBS scores
subscale shared an average of 38% of the when ACIDS scores were partialled out.
variance of CTBS scores. Weaker correla- In addition, the Total APRS score and the
tions were obtained between APRS scores Academic Productivity subscale shared 9%
and direct observations of on-task behav- of the variance with AES beyond that
ior with only an average of 7.2% of the accounted for by teacher ratings of
latter’s variance accounted for.
problem behavior.
Academic
Correlations
Ratings
On Task Percentage
Rating Scale
TABLE 4
Between APRS Scores and Criterion
with ACTRSa Scores Partialled Out
Total
Score
Measures
ADHD
Performance
Academic
Success
293
Measures
Impulse
Control
Academic
Productivity
-.12b
0.24
0.24
-. 07
0.04
0.01
0.03
9.04
AESC
.32*
.06
.22
.37**
CTBS Math
.38**
.56***
.I4
.25
CTBS Reading
.46***
.58***
.24
.34*
CTBS Language
.43**
.54***
.28
.30*
*Abbreviated
bCorrelations
Conners Teacher Rating Scale.
are based on N = 50 with degrees of freedom
‘Academic
Efficiency Score.
*p < .05
*+p < .Ol
Note:
National percentile
““p
= 48.
< a01
scores were used for all Comprehensive
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 coefficients 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. Specifically, the relationship between CTBS
Math scores and Academic Success ratings 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 relationship with Impulse Control (t(47) = 2.50,
p < .05, Academic Productivity (t(47) =
2.38, p < .05, and ACTRS (t(47) = 2.76,
Test of Basic Skills (CTBS) subscales.
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 relationships 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., absence of problem behaviors
and accurate work completion). It should
be noted that validity coefficients associated with the Impulse Control subscales
were not found to be significantly greater
than either of the other subscales.
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 difficulties). 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 received teacher ratings of ADHD symptomatology within 1 standard deviation of
the mean (n = 390). Separate t-tests were
conducted employing each of the APRS
scores as dependent measures. Statistically 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 questionnaire that provides reliable and valid
information about the quality of a student’s academic performance and behavioral conduct in educational situations.
Separate principal components analyses
resulted in the extraction of three components or subscales (i.e., Academic
Success, Impulse Control, and Academic
Productivity) that were congruent across
random subsamples. The Academic Success 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 Productivity and Impulse Control subscales.
Although the latter are highly correlated
with the Academic Success subscale, both
appear to provide unique information
regarding factors associated with the
process of achieving classroom success
(e.g., work completion, following instructions, behavioral conduct).
Psychometric
Properties
of the APRS
The APRS total and subscale scores
were found to possess acceptable 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 coefficients and to share appreciable variance
with several subtests of a norm-referenced
achievement test and a measure of
classwork accuracy. Further, when validity 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 subscales were
found to exhibit divergent validity. Although all APRS subscales were positively
correlated with achievement test scores,
the strongest relationships were found
between the Academic Success subscale
and CTBS percentile rankings, accounting
for an average of 38% of the variance.
Alternatively, although negative correlations 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 completion accuracy (AES) had a significantly
greater correlation with the Academic
Productivity subscale with 32.5% variance
Academic
Performance
accounted for. This latter finding may
appear counterintuitive (i.e., that Academic 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
Success subscale 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 associated with the process of day-to-day
academic performance. Finally, although
the Impulse Control subscale was significantly 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 percentage were obtained for the APRS Total and
Academic Productivity scores, the Academic Success and Impulse Control
subscales were not related to this observational measure. One explanation for this
result is that the Academic Productivity
subscale is more closely related to factors
associated with independent work productivity (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
Rating Scale
295
more specific index such as on-task
frequency.
Teacher ratings on the APRS differentiated 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’s discriminant validity and value for
screening/problem
identification
purposes. 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 competent than boys on the Academic Productivity 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 differences in daily academic performance do
not impact on teachers’ overall assessment of educational status until the later
grades when demands for independent
work greatly increase. Interestingly, no
significant grade differences were obtained for any of the APRS scores. As
Hightower and colleagues (1986) have
suggested, a lack of differences across
grade levels implies that teachers complete ratings of academic performance in
relative (i.e., in comparison with similaraged peers) rather than absolute terms.
296
School Psychology
Review, 7997, Vol. 20, No. 2
supplement to behavioral assessment
techniques
(e.g., direct observations of
Several factors limit definitive conclubehavior,
curriculum-based
measuresions about the utility of the APRS based
ment)
given
its
brevity,
focus
on both
on the present results. First, the sample
global
and
specific
achievement
parameof children studied was limited to an
ters,
and
relationship
with
classroomurban location in one geographic region;
it is unknown how representative these based criteria of academic success. The
normative data would be for children from present results provide initial support for
rural or suburban settings as well as other the utility of the APRS as a screening/
regions. Previous research with similar problem identification measure. Further,
teacher questionnaires would suggest when used in the context of an assessment
significant differences in scores across battery that includes more direct meaurban, suburban, and rural settings (e.g., sures of academic performance, the APRS
Hightower et al., 1986). Secondly, for the may provide important data regarding the
norms to be generally applicable, APRS social validity (i.e., teacher perceptions of
ratings would need to be collected for a changes in academic status) of obtained
sample representative of the general intervention effects, although its increpopulation with respect to ethnicity and mental validity would need to be established. The APRS’s sensitivity to the effects
socioeconomic status. A further limitation
of the present study was the limited range of behavioral and psychopharmacological
of criterion measures employed. In par- interventions awaits further empirical
ticular, the relationship of APRS scores study.
with more direct measures of academic
performance (e.g., criterion-based measurement) should be explored, as the
weaknesses of norm-referenced achieve- American Psychiatric Association. (1987). Diugnosment tests for this purpose are well
tic and statistical manual of mental disorders
(3rd ed. Revised). Washington, DC: Author.
documented (Marston, 1989; Shapiro,
1989). Finally, additional psychometric
Barkley, R. A. (1988). Child behavior rating scales
properties of this scale, such as predictive
and checklists. In M. Rutter, A. H. Tuma, & I. S.
validity and inter-rater reliability, need to
Lann (Eds.), Assessment and diagnosis in child
psychopathology
(pp. 113-155).
New York:
be documented. Empirical investigations
Guilford.
are necessary to determine the usefulness
of the APRS as a treatment-sensitive
Bernstein,
I. H. (1988). Applied
multivariate
instrument. Evidence for the latter is
analysis. New York: Springer-Verlag.
especially important as a primary purpose
for creating the APRS was to allow Cantwell, D. P., & Satterfield, J. H. (1978). The
prevalence of academic under-achievement
in
assessment of intervention effects on
hyperactive children. Journal @‘Pediatric pszlcholacademic performance.
w, 3, 168-171.
Limitations
of the Present Study
Summary
The results of this preliminary investigation indicate that the APRS is a highly
reliable rating scale that has demonstrated 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
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
nominal scales. Educational
Measurement, 20,37-46.
of agreement for
and pS@&gical
CTB/McGraw-Hill.
(1982). l%e comprehensive
of Basic Skills. Monterey, CA Author.
Test
DuPaul, G. J. (in press). Parent and teacher ratings
of ADHD symptoms: Psychometric properties in a
community-based
sample. Journal
of Clinical
Child Psychologg.
Academic
Performance
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.
Rating Scale
297
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
measurement approach to assessing academic performance: 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.
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.
National Commission on Excellence in Education.
for
(1983). A nation at risk: 17Le immative
educational reform. Washington, DC: Author.
Goyette, C. H., Conners, C. K., & Ulrich, R. F. (1978).
Normative data on Revised Conners Parent and
Teacher Rating Scales. Journal of Abnormal Child
Psychdogy, 6,221-236.
Neeper, R., & Lahey, B. B. (1986). The Children’s
Behavior Rating Scale: A factor analytic developmental study. school Psychology Reuiew, 15, 277288.
Gresham, F. M., & Elliott, S. N. (1990). Social skills
rating system. Circle Pines, MN: American Guidance Service.
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.
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.
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.), Hwactivity
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 methylphenidate. 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 hyperactivity children: A within-subject
comparison.
Journal of Applied Behavior Analysis, 15, 205216.
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.
290
School Psychology Review, 7997, Vol. 20, No. 2
Wallrer, H. M., & McConnell, S. R. (1988). ?ViuZtiM&ml1
&ale of social GmqMmce and &hool
AAustin, TX: Pro-Ed, Inc.
Weiss, G., & Hechtman, L. (1986). Hyperactive
clddm
grown up. New York: GuMord.
Weissberg,R.P.,Cowen,E. L., Lotyczewski,B. S.,Boike,
M. F., Orara, N., Ahvay, Stalonas, P., Sterling, S.,
& Gesten,E. L (1987). Teacherratings of children’s
problem and competence behatiors: Normative
and parametric characteristics.AmericanJoumMtl
c#cOmmun~pszlcho~,
15,387-401.
Whalen, C. K., Henker, B., & Granger, D. A. (1989).
Ratings of medication effects in hyperactive
children: Viable or vulnerable?Behavioral Assessment, 11,179.199.
e J. DuPauI, PhD, received his doctorate from 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.
-7
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.
Academic Performance Rating Scale
APPENDIX A
Student
Date
Teacher
Grade
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) relative to classmates.
049%
Estimate the percentage of
written language arts work
completed (regardless of accuracy) relative to classmates.
049%
I
2
3
.Estimate the accuracy of com4 pleted written math work
(i.e., percent correct of work
done).
044%
65-69%
70-79%
2
3
4
4. Estimate the accuracy of completed written language arts
work (i.e., percent correct of
work done).
044%
5. How consistent has the quality of this child’s academic
work been over the past
week?
Consistently
Poor
1
1
5049%
2
5049%
3
70-79%
8049%
4
804%
4
8049%
90-100%
5
90400%
5
90-100%
5
70-79%
8&89%
2
3
4
5
More Poor
than
Successful
Variable
More
Successful
than Poor
Consistently
successful
1
2
3
4
5
Never
Rarely
Often
Very often
1
2
Never
Rarely
1
2
3
4
5
8. How quickly does this child
learn new material (i.e., pick
up novel concepts)?
Very Slow
Slow
Average
Quickly
very
Quickly
1
2
3
4
5
9. What is the quality or neatness of this child’s
handwriting?
Poor
Fair
Average
Above
Average
Excellent
I
2
3
4
5
6. How frequently does the student accurately follow teacher
instructions and/or class discussion during large-group
(e.g., whole class) instruction?
7. How frequently does the student accurately follow teacher
instructions and/or class discussion during small-group
(e.g., reading group)
instruction?
1
6549%
70-79%
Sometimes
3
Sometimes
90400%
4
5
Often
Very often
300
SchoolPsychologyReview,7997,
10. What is the quality of this
child’s reading skills?
Vo/.2OJVo.2
Poor
Fair
Average
Above
Average
Excellent
1
2
3
4
5
Poor
Fair
Average
Above
Average
Excellent
1
2
3
4
5
12. How often does the child
complete written work in a
careless, hasty fashion?
Never
Rarely
1
2
13. How frequently does the
child take more time to complete work than his/her
classmates?
Never
Rarely
1
2
14. How often is the child able to
pay attention without you
prompting
him/her?
Never
Rarely
1
2
15. How frequently does this
child require your assistance
to accurately complete his/
her academic work?
Never
Rarely
1
2
16. How often does the child
begin written work prior to
understanding
the directions?
Never
Rarely
1
2
17. How frequently does this
child have difficulty recalling
material from a previous day’s
lessons?
Never
Rarely
1
2
18. How often does the child appear to be staring excessively
or “spaced out”?
Never
Rarely
1
2
19. How often does the child appear withdrawn or tend to
lack an emotional response in
a social situation?
Never
Rarely
1
2
11. What is the quality of this
child’s speaking skills?
Sometimes
3
Sometimes
3
Sometimes
3
Sometimes
3
Sometimes
3
Sometimes
3
Sometimes
3
Sometimes
3
Often
4
Often
4
Often
4
Often
4
Often
4
Often
4
Often
4
Often
4
Very Often
5
Very Often
5
Very Often
5
Very Often
5
Very Often
5
Very Often
5
Very Often
5
Very Often
5