Academic Performance Rating Scale

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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
scales
have
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
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$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
APRS
is
a
19-item
scale
that
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.
Seven
APRS
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
assess
teacher
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
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
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
assess
test-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
assessed
weekly
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
.74
was
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
APRS
ratings
as
well
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
Success
factor
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
scale
were
compared
using
separate
t-tests for
correlated
measures.
Scores
for
each
APRS
scale
were
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)
assess
the
effects
of
these
variables
on
APRS
ratings
and
(b)
provide
norma-
tive
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
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.,
absence
of
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
Success
subscale,
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
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
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
subscales
were
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
CTBS
percentile
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
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
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-
age
were
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’s
discriminant
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
be
for
children
from
rural
or
suburban
settings
as
well
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
as
a
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’s
sensitivity
to
the
effects
of
behavioral
and
psychopharmacological
interventions
awaits
further
empirical
study.
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B.
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measure-
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academic
perfor-
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What
it
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why
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T.
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M.
D.,
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A.,
&
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J.
S.
(1982).
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vs.
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cost
in
the
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of
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A
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216.
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B. V.
(1981).
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engaged
time,
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R.
A,
&
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T.
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7997,
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H.
M.,
&
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S.
R.
(1988).
?ViuZti-
&
Gesten,
E.
L
(1987).
Teacher
ratings
of
children’s
M&ml1
&ale
of
social
GmqMmce
and
&hool
problem
and
competence
behatiors:
Normative
A-
Austin,
TX:
Pro-Ed,
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and
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Weiss,
G.,
&
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L.
(1986).
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c#cOmmun~pszlcho~,
15,387-401.
clddm
grown
up.
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GuMord.
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C.
K.,
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B.,
&
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R.
P.,
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E.
L.,
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S.,
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effects
in
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children:
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or
vulnerable?
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M.
F.,
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-7
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.
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