States
continue to struggle with the best ways to include students with disabilities in
state testing programs. The menu of testing options available for students with
disabilities includes taking the regular assessment without accommodations,
taking the regular assessment with accommodations, or taking a different
assessment. Many students with disabilities take the regular assessment under
standard conditions, but a large percent take the regular assessment under
accommodated conditions (Thurlow, House, Boys, Scott, & Ysseldyke, 2000).
Because the validity evidence for a test is usually tied to an administration
without accommodations, it is necessary to gather additional validity evidence
for test scores obtained under accommodated conditions. With federal legislation
requiring states to report disaggregated results for students with disabilities,
the need for additional validity evidence is even greater.
In the
research literature on test accommodations, the term accommodation refers to
that subset of test changes that do not
alter the construct the test was designed to measure. Alterations that
change the construct of the test are sometimes called modifications, although
some states use the term “modification” for changes considered appropriate (Thurlow,
& Weiner, 2000). Generally, an accommodation can be defined as an alteration to
the standard test conditions that neutralizes extraneous sources of difficulty
resulting from an interaction between standard administration and a student’s
disability while preserving the measurement goals of the test (Elliott,
Kratochwill, McKevitt, Schulte, Marquart, & Mroch, 1999; Phillips, 1994; Tindal,
Helwig, & Hollenbeck, 1999; Willingham, Ragosta, Bennett, Braun, Rock, & Powers,
1988). Validation studies are necessary to ascertain the appropriateness of an
alteration. Unfortunately, the evidence substantiating the validity of an
accommodation is scant, even for some of the most widely used alterations, such
as the readaloud administration of a test.
Two
types of validation evidence for testing accommodation can be derived from the
definition of test accommodations. One type of evidence is the test score boost.
If accommodations remove extraneous sources of difficulty resulting from an
undesirable interaction between standard testing conditions and a student’s
disability, then a valid accommodation should result in a performance boost for
students with that disability. Because a valid accommodation acts only on the
interaction between disability and standard testing conditions, it should not
result in a test score boost for students without a disability (Phillips, 1994).
Such test score boosts represent necessary but insufficient data to conclude
that an accommodation is valid.
A valid
test accommodation should preserve the measurement goals of the test as well as
result in a performance boost. In other words, it should not alter the construct
the test was designed to measure. There are several ways to ascertain whether a
set of test items measures the same construct for different examinee groups. One
way is to apply differential item function analysis, known as DIF. DIF analysis
represents a way to compare item characteristics such as item difficulty across
groups. Under the notion of DIF, groups are first equated on ability, which is
usually estimated by each person’s overall test score. Presumably, if all of the
items measure the same construct in the same way for all groups, then each item
should be equally difficult across the abilitymatched groups. Finding that many
of the items display DIF would be an indication that the test is measuring
something different across groups.
DIF
analysis requires large samples, at least several hundred cases per group. Most
experimental studies contain fewer than 100 participants. State testing
databases contain thousands of cases, making it possible to conduct DIF
analysis. In addition to containing large samples, such databases represent the
reallife testing situations. It is essential that test accommodations
demonstrate validity in the real testing environment. Extant databases represent
a wonderful source of information for ascertaining the validity of testing
accommodations in natural settings.
There
are some shortcomings to evaluating the validity of testing accommodations using
extant data. Foremost is the fact that there may not be naturally occurring
appropriate control groups. Students with disabilities who take a test with an
accommodation differ from students without disabilities who do not use
accommodations in three common ways: (1) they have a disability, (2) they took
the test with an accommodation, and (3) as a group their overall performance is
usually (but not always) much lower than their peers without disabilities. In
DIF studies that do not account for each of these differences, it would not be
appropriate to attribute DIF to the presence of an accommodation because the
accommodation is confounded with other factors. One possible solution is to
define successive groups wherein one group differs from the reference group only
in terms of performance, another that differs in terms of performance and
presence of a disability, and one that differs in terms of performance, presence
of a disability, and presence of an accommodation. Although it is not possible
to ensure that these groups differ only in the ways indicated because students
are not randomly assigned to groups, the three layers of control groups do make
it possible to begin to isolate the effect of the test accommodation on item
functioning. However, even if DIF is attributed in this way to the presence of a
testing accommodation, one cannot conclude that the accommodation itself is
invalid.
There
are three possible explanations for why differential item functioning might
exist. It may be that the accommodation simply does not do what proponents think
it should do. In other words, the accommodation may change the construct that
the test was designed to measure. Another possibility is that the accommodation
was not appropriately administered. For example, an accommodation that requires
a proctor to read math test items may be ineffective because the proctor read
the items too quickly. A third possibility is that the accommodation may have
been administered to students who do not actually need it. Research on how
accommodations decisions are made indicates that the decision makers, members of
the IEP team, tend to overaccommodate students (Elliott et al., 1999; Fuchs ,
Kratochwill, McKevitt, Schulte, Marquart, & Mroch 2000; Fuchs, Eaton, Hamlett, &
Karns, 2000). In other words, they use accommodations that have no benefit for
the student. Regardless of knowing the reason
why, it is still important to ascertain whether accommodated test scores
measure the same construct as nonaccommodated test scores.
The
readaloud accommodation is one of the most commonly used accommodations
(Bielinski, Ysseldyke, Bolt, Friedebach, & Fredebach, 2001). Prior to this
study, only two studies of the readaloud accommodation used test data from an
actual statewide administration of an achievement test to evaluate the validity
of the accommodation. Both of those studies used structural equation models to
evaluate factor invariance between the scores of nonaccommodated students and
the scores of students receiving the readaloud accommodation. Tippets and
Michaels (1997) studied the effect of the readaloud accommodation on a reading
test and a language usage test. The tests were part of the Maryland School
Performance Assessment Program. Pomplun and Omar (2000) studied the effect of
the readaloud accommodation on a math test that was part of the Kansas
Assessment Program. Each study reported that the factor structure was the same
for the nonaccommodated and the accommodated groups. Both studies fitted a
twofactor model to the item sets because a twofactor model resulted in a
significant improvement in model fit. However, the reality is that scores from
these assessments are based on a single latent trait; therefore, their findings
cannot be generalized to the scores actually reported in those states.
The
method used in this study produces results that demonstrate whether test items
function the same under nonaccommodated and accommodated conditions when a
single latent trait model is used. In this way, the results can indicate the
validity of the test scores reported in the assessment system when the scores
were obtained under accommodated conditions.
The
readaloud accommodation, as with any accommodation, should result in scores
that are more meaningful for students who need the accommodation as compared to
the scores obtained by similar students taking the test without the
accommodation. Tindal, Heath, Hollenbeck, Almond, and Harniss (1998) emphasized
the importance of the area of student need as defined by the student’s
Individualized Education Program (IEP) for making the determination about which
accommodations to give; students with the same primary IEP area probably have a
common need and should be given the same accommodation. In the Tindal et al.
(1998) study, the effect of the readaloud accommodation on the construct
validity of a reading and a math test was evaluated by comparing item difficulty
invariance across groups. One group included students whose primary IEP area was
reading and who received the readaloud accommodations; another group also had
reading as their primary IEP area but did not receive an accommodation; the
other two groups consisted of students without disabilities taking the test
without an accommodation.
Research Questions
Presumably, all students whose primary IEP area is reading should benefit from
the readaloud accommodation. The performance of students with a reading
disability not receiving the readaloud accommodation should be affected by
extraneous sources of difficulty that alter item characteristics. Therefore, the
item characteristics based on the students who received the accommodation should
closely resemble the item characteristics in the comparison group of
nondisabled students, whereas the item characteristics for students with a
reading IEP not receiving the accommodation should differ markedly from those in
the comparison group.
The
research questions for this study are based on the preceding rationale. The two
specific research questions are:
• Is item difficulty the same
for students receiving the readaloud accommodation as for nonaccommodated
students without disabilities?
• Does item difficulty markedly
differ for students who need the readaloud accommodation, but did not receive
it when compared to the nonaccommodated students without disabilities?
Method
Participants
This
study uses data from the 1998 administration of the Missouri Assessment Program
(MAP). All public school students are required to participate in MAP. Students
may participate in the program by taking the regular assessments without
accommodations, the regular assessments with accommodations, or an alternate
assessment. For students with disabilities, the decision about how the student
should participate is usually made by the student’s IEP team. The students and
their parents are encouraged to participate in the decision process.
When
students with disabilities take the test, their test forms are marked to
indicate (1) the primary area of their special instruction (e.g., reading, math,
behavior), adding with their category of disability. In addition, the
accommodations used by students are indicated. The focus of the study is on
those students who primarily receive special education services for reading
instruction. For each examinee, the proctor indicated the student’s primary IEP
instructional area. Only those students for whom the primary instructional area
was reading were used in the two special education groups defined below.
In
1998, there were 52,387 third grade students with a valid communications arts
(reading) test score, and 66,800 fourth graders with a valid math test score.
Students receiving special education services constituted 11.4% (N=5,962) of the
population taking the communication arts test, and 12.7% (N=8,491) of the
population taking the mathematics test. From these data, four groups of students
were defined. Group A represented a random sample of approximately 1000 general
education students who took the test without an accommodation. Group B
represented a random sample of approximately 1000 general education students who
took the test without an accommodation and who were matched in ability to the
group of students with an IEP in reading. The selection of the sample of cases
for Group B was done so that the distribution of the numbercorrect scores on
the multiplechoice items matched the pooled distribution of the numbercorrect
scores for groups C and D. Group C represented all of the students whose primary
IEP instructional area was reading and who took the test without an
accommodation. Group D represented all of the students whose primary IEP
instructional area was reading and who took the test under the readaloud
accommodation (either alone or in combination with extended time, small group
administration, or both). Most of the students receiving the readaloud
accommodation also received extra time and took the test in a small group.
Table 1
is a summary of group performance on the each test measured as the number of the
multiplechoice items answered correctly. Groups B, C, and D have similar means
and standard deviations on both the reading test and the mathematics test. The
mean numbercorrect score for Group A was about one standard deviation unit
greater than the mean score in the other three groups on both tests. The ratio
of males to females in groups C and D was about 2 to 1. These are the groups
comprised of students with an IEP in reading. This ratio is similar to the ratio
of males and females in special education. Additionally, just over 90% of the
students in each group had a learning disability (LD). Groups A and B consisted
of nearly equal numbers of males and females.
Table 1. Performance and Gender Makeup
of Each Group

Group 

A^{a}
(General Ed. No Accomm.) 
B^{b}
(Low Perf. No Accomm.) 
C^{c}
(IEP No Accomm.) 
D^{d}
(IEP with Accomm.) 
Communication Arts^{e} 




Mean 
31.0 
23.0 
21.6 
23.7 
SD 
7.13 
8.05 
7.76 
7.50 
N 
1002 
995 
600 
661 
% Female 
49 
45 
34 
33 
Mathematics^{f} 




Mean 
24.9 
19.2 
18.9 
20.1 
SD 
5.13 
6.23 
6.34 
5.99 
N 
1139 
1006 
831 
1082 
% Female 
49 
50 
33 
32 
^{
a general education students taking
the test without accommodations
b low performing nondisabled students taking the test without an
accommodation
c IEP students taking the test without an accommodation
d IEP students taking the test with the readaloud accommodation
e highest possible score = 41
f highest possible score = 32
Instruments
The
reading and mathematics tests used in the Missouri Assessment Program (MAP)
consist of a combination of normreferenced test items developed by CTBMcGraw/Hill
and items developed expressly to measure Missouri’s Show Me Standards. Tests
were divided into three sections referred to as sessions. Session 1 consisted of
a combination of performance events and constructed response items, Session 2
consisted of only constructed response items, and Session 3 consisted of only
multiplechoice items. The Session 3 items were adapted from the Terra Nova™ to
meet Missouri’s achievement standards and they are included to provide national
normative comparisons. The present study used only the multiplechoice items.
The
reading test administered in 3rd
grade consisted of 41 multiplechoice items that assess reading comprehension on
six reading passages. Passages were either short fictional stories with fewer
than 250 words or short poems. Students were required to answer both literal and
inferential test items.
The
mathematics test that is administered in 4th
grade consisted of 32 multiplechoice items. Test items were chosen so that the
following five areas were represented: number sense, geometric/spatial sense,
patterns and relationships, mathematical systems and number theory, and discrete
mathematics.
Procedures
The
primary objective of this study was to ascertain the effect that the readaloud
accommodation had on the validity of math test scores and reading comprehension
test scores. It is usually assumed that achievement tests (e.g., a math test)
measure a single ability, and the ability that is measured is the same in each
subpopulation. When a test measures a different ability in two subpopulations,
score interpretation becomes very difficult. It does not mean that the test is
valid in one population but not in the other, only that the meaning of the
scores is different across populations. This difference makes it difficult to
discuss the effects of various conditions on test score validity. The resolution
is to select one group as the standard against which the interpretation of the
validity of scores for other groups is described. This standard group is called
the reference group.
Many
methods have been developed to ascertain whether a test measures the same trait
in two groups. The method chosen in this study uses item response theory. Item
response theory constitutes a set of mathematical models and assumptions that
define the probability of getting an item correct as a function of an examinee’s
ability and item characteristics. When the assumptions hold and the model fits
the data, the item characteristics are not dependent on the characteristics of a
particular population. One such item characteristic is item difficulty.
It has
been argued that an item difficulty difference of sufficient magnitude indicates
that the item is measuring a different ability for the two groups. These items
are referred to as DIF (differential item functioning) items. The presence of
many DIF items suggests that the test measures different abilities for different
groups of examinees. This study compared the item difficulty estimates from each
of the four groups defined above. The question to be answered was whether item
difficulty estimates generated using a unidimensional threeparameter logistic
model were substantially different for students receiving the readaloud
accommodation compared to students who took the test without an accommodation.
Accommodations research using extant data is complicated by the fact that the
use of test accommodations is confounded by the presence of a disability and low
achievement. Students without a disability usually do not receive accommodations
and score wellabove students with disabilities. Thus, students receiving
accommodations typically differ from nonaccommodated students in three ways:
(1) lower performance, (2) the presence of a disability, and (3) the presence of
an accommodation. Identifying four groups of examinees allows us to begin to
isolate the effect of the readaloud accommodation. First, a random sample of
students without disabilities who do not use accommodations was selected to
serve as the reference group (Group A). The item structure for this group serves
as the standard for what the test was designed to measure. A random sample of
low ability students without disabilities was used to study the effect for the
overall performance difference (Group B). A group of students with an IEP in
reading who took the test under standard conditions was used to study the effect
that the disability has on the construct being measured (Group C). Item
difficulty differences between Group C and Group A represent the effect of the
disability plus the effect for the overall performance difference; therefore,
the effect found for the former group must be subtracted from the effect for
this group in order to study the effect for the disability. Last, a group of
students with an IEP in reading who received the readaloud accommodation (Group
D) was used to study the effect that the accommodation had on the construct
being measured. Item difficulty differences between Group D and Group A include
any effect of the accommodation plus the effect of the disability plus the
effect of the overall performance difference; therefore, the effect attributed
to Group C must be subtracted from the effect for Group D in order to study the
effect for the accommodation.
The
program, BILOGMG, was used to fit the threeparameter logistic model to the
data (Zimkowski, Muraki, Mislevy, & Bock, 1996). BILOGMG makes it possible to
produce item difficulty estimates on several groups simultaneously; all that is
required is to define one group as the reference group and the other groups as
focal groups. The reference group is used to set the location (mean) and the
metric (standard deviation) of the item difficulty scale. Item difficulty
estimates for each focal group are placed onto the scale of the reference group
by applying two sets of constraints. First, BILOGMG constrains the item
discrimination and pseudoguessing parameters to be equal across groups for each
item. Second, it constrains the sum of the item difficulty estimates in the
focal groups to equal the sum of the item difficulty estimates in the reference
group. After applying these constraints, the resulting item difficulty estimates
are on a common scale and mathematical operations can be applied to them.
The
effect that the overall performance difference, the presence of the disability,
and the use of the readaloud accommodation had on item difficulty can be
ascertained by comparing the item difficulty difference between conditions
(focal group item difficulty minus reference group item difficulty). Because the
difference may be either negative or positive, it is necessary to square the
difference so that the sum will produce a positive number that indicates the
overall magnitude across all test items. The square root of the average squared
discrepancy is referred to as the rootmeansquared discrepancy (RMSD). The
formula for the RMSD is shown here:
where j
represents the jth
item, k is the number of items on the test, bjF
is the logit item difficulty estimate on the jth
item for the focal group and bjR
is the logit item difficulty estimate on the jth
item for the reference group. The RMSD gives a measure of the overall item
difficulty difference across all test items. A large RMSD suggests that the
items operated differently in the focal group compared to the reference group.
The
magnitude of the RMSD is a function of the effect of interest as well as
measurement and sampling error. One would expect the RMSD to be some value
greater than zero even between two randomly equivalent groups. An estimate of
the amount of the RMSD that could be attributed to estimation error was
ascertained by computing the RMSD between two random samples of approximately
1000 general education students. This value is analogous to the error term in
the denominator of a ttest. A judgment of statistical significance could be
made by computing the ratio of the RMSD from one of the focal groups to the RMSD
that results from estimation error.
Another
way to ascertain the magnitude of each effect would be to compare the fit of the
3parameter logistic (PL) model when the item difficulties are constrained to be
equal across groups versus the model in which item difficulties are allowed to
vary across groups. BILOGMG calculates the –2 log likelihood statistic, which
can be used to judge the goodness of fit of the model to the data. In order to
ascertain the magnitude of each effect, one analysis was conducted in which the
item difficulty estimates for the reference group (Group A) and each focal group
(Group B, C, and D) were constrained to be equal across groups. A second
analysis was conducted using the DIF option in BILOGMG. The DIF option allows
the item difficulties to vary across groups. The difference in the –2 log
likelihood statistic between the two analyses provides an indication of the
overall magnitude of the effect. Twelve analyses were conducted, two for each
effect on both the reading and math test items.
BILOGMG
computes the item difficulty difference across groups (bjF
 bjR) after the items have been rescaled to a common scale, and it
generates the standard error of this difference. The standard error can be used
to determine whether the difference statistically differs from zero (no
difference). Items for which the ratio of the difficulty difference to the
standard error exceeds 2.0 indicate that the item is measuring something
different in the two groups, which is known as differential item functioning, or
DIF for short. The number of DIF items was also used to ascertain the magnitude
of the effect of each condition; the presence of many DIF items is compelling
evidence that a test functions differently for two groups.
Average Squared Discrepancy Results
Reading Test
The
effect of the readaloud accommodation on item difficulty was described in three
ways. First, discrepancies between the item difficulty estimates for Group A and
each of the focal groups (Groups B, C, and D) were computed. Table 2 summarizes
the results from the reading test. The table reports the median
squared discrepancy and not the mean squared discrepancy because the
distribution of the item difficulty differences was positively skewed. The
median squared discrepancy between the low performing general education students
(Group B) and the random sample of general education students (Group A) was only
.009, thus indicating that the rescaled item difficulty estimates were very
similar for these groups. Because these values are squared, it is necessary to
take the square root to obtain the magnitude of the discrepancy; therefore, the
median squared discrepancy for Group B corresponds to an average item difficulty
difference between Group B and Group A of about .10 (i.e., the square root of
.009). The median squared discrepancy for the group of students with an IEP in
reading taking the reading test without an accommodation (Group C) was .042, or
four times greater than the median squared discrepancy between Group B and Group
A. The students with an IEP in reading who took the reading test with the
readaloud accommodation (Group D) had a median squared discrepancy of .086,
which was 10 times greater than the median squared discrepancy between Group B
and Group A.
Table 2. The Average Squared Discrepancy Between the
Item Difficulty Estimates Obtained on the Focal Groups (Groups B, C, and D) and
the Reference Group (Group A) on the Reading Test
Squared Discrepancy
Effect
Median
Maximum
75th Percentile
Performance alone1
.009
.099
.021
Perf. + Disability2
.042
1.724
.091
Perf. + Dsbl. +
Accommodation3
.086
4.792
.310
1Perf = Group B compared to Group A
2Perf + Disability = Group C compared to Group A
3Perf + Dsbl + Accommodation = Group D compared to Group A
The
distribution of squared discrepancies was skewed, as evidenced by the magnitude
of the maximum value compared to the value at the 75th
percentile. For Group C the value of squared discrepancy corresponding to the 75th
percentile was .091 on the reading test, whereas the maximum value was 1.72. In
other words, the largest squared discrepancy (1.72) was 19 times larger than the
squared discrepancy corresponding to the 75th
percentile. In Group D, the maximum squared discrepancy was 4.79, which was more
than 15 times larger than the squared discrepancy corresponding to the 75th
percentile. A cursory examination of Appendix A indicates that the last three
questions on the reading test were much more difficult for the students with an
IEP in reading (groups C and D) than for the general education students. It is
important to note that these differences are not the result of overall
performance because the rescaling controls for overall performance differences.
It is not clear why the last three items were so much harder for the students
with an IEP in reading. An examination of frequency of not attempting each item
indicates that this alone cannot account for the large difficulty difference.
About 1% of the students in Groups A, B, and C did not attempt these items,
compared to over 2% of the students in Group D. Removing the last three items
from the results would dramatically reduce the amount of skew in the
distribution of squared discrepancies.
Math Test
Table 3
contains the summary of the squared discrepancies between the item difficulty
estimates for each of the focal groups (Groups B, C, and D) and the reference
group (Group A). The median squared discrepancy for Group B was quite small
(.008), which is about the same magnitude as the median squared discrepancy for
Group B on the reading test. A value this small indicates that the rescaled item
difficulty estimates were similar between Group B and Group A. Unlike the
results on the reading test, the median squared discrepancies for Group C and
Group D were relatively small, .023 and .022 respectively. A squared discrepancy
of this magnitude indicates that, on average, the item difficulty difference
between the reference group and each of the focal groups was in the order of .10
to .20. The distribution of squared discrepancies was not as skewed on the math
test as it was on the reading test, thus indicating that no item or subset of
items would dramatically influence the results (see Appendix B).
Table 3.The Average Squared Discrepancy Between the Item
Difficulty Estimates Obtained on the Focal Groups (Groups B, C, and D) and the
Reference Group (Group A) on the Math Test
Squared Discrepancy
Effect
Median
Maximum
75th
Percentile
Performance alone1
.008
.425
.039
Perf. + Disability2
.022
.521
.124
Perf. + Dsbl. + Accommodation3
.023
.286
.071
1Perf = Group B compared to Group A
2Perf + Disability = Group C compared to Group A
3Perf + Dsbl + Accommodation = Group D compared to Group A
Fit Results
Two fit
indices were used to estimate the magnitude of each effect—the effect due to the
performance difference, the effect due to the disability, and the effect due to
the readaloud accommodation. One index, called the root mean squared
discrepancy, gauges the overall discrepancy between item difficulty estimates
between the reference group and each focal group. When the model fits the data
and there is no estimation error, the RMSD should equal zero. However, a portion
of the variance in these item difficulty differences can be attributed to the
estimation error. The magnitude of the estimation error is a function of both
the number and quality of the items, and the number of examinees. In order to
estimate how much of the RMSD can be attributed to estimation error, the RMSD
between two random samples of general education students each with about 1000
students was computed. The RMSD between these two samples provides an estimate
of the amount of estimation error present in the other comparisons.
The –2
log likelihood difference between the unconstrained and the constrained model
was also used to evaluate each effect. For the unconstrained model, item
difficulty was allowed to vary between the reference group and the focal group.
For the constrained model, the item difficulty estimates were constrained to be
equal across groups. A large –2 log likelihood indicates that the item
difficulties are not the same between the focal group and the reference group.
Table 4
displays the RMSD and the –2 log likelihood fit index (G2c
– G2u)
for each focal group/reference group comparison. The effects present in each
comparison are shown in the left column. Each effect was isolated by subtracting
the RMSD of the comparison that contains all of the effects except for the
effect to be isolated. For instance, the effect due to the learning disability
was calculated by subtracting the RMSD due to performance from the RMSD due to
performance and disability. The statistical significance of this effect was
determined by calculating the ratio of that difference to the RMSD attributable
to estimation error. The ratio follows a tdistribution; therefore, an absolute
value greater than 2.0 indicates a statistically significant effect. For a
onetailed hypothesis test with 40 degrees of freedom on the reading test, the
critical t is 1.684, and for the 31 degrees of freedom on the math test the
critical t is 1.696.
Table 4. The RMSD Between the Reference
Group and the Focal
Group, and the –2 Log
Likelihood
Fit
Between the Constrained (G2c) and the Unconstrained
Model (G2u)
Reading
Math
Effect
RMSD
G2CG2U
RMSD
G2CG2U
Estimation error
.11
.19
Performance alone
.131
7122
.20
614
Perf + Disability
.361
8432
.22
605
Perf + Disability +
Accommodation
.671
12612
.30
612
Reading Test
For the
reading test, the effect due to estimation error resulted in an RMSD of .11. The
effect due to the overall performance difference was .13, which is about the
same magnitude as that due to estimation error, indicating that the effect was
not significant. The RMSD was roughly three times larger (RMSD = .36) when the
presence of the disability was added, and it was roughly six times larger (RMSD
= .67) when the effect for the disability plus the effect for the accommodation
was included. The effect for reading disability was .23 (.36  .13) and it was
significant (t = 2.09). The effect due to the readaloud accommodation was .31
(.67  .36), which is also significant (t = 2.82).
The –2
log likelihood difference between the constrained and unconstrained model for
each effect displayed a similar pattern of increasing magnitude. When the two
groups (reference and focal) differ only on ability, the difference in the –2
log likelihood was 712, it increased to 843 with the addition of the effect for
disability, then jumped to 1261 with the addition of the effect for the
readaloud accommodation. The difference between the –2 log likelihood is
distributed as a chisquare. The degrees of freedom for the chisquare test is
equal to the number of items on the test. Each of the effects resulted in
significantly poorer model fit. In other words, the item difficulties are not
the same across groups.
The
last three items on the reading test had much larger item difficulty differences
(Reference Group – Focal Group) than the other items. The item difficulty
difference was .83, 1.02, and –1.31 in the comparison between Group C and
Group A, and it was –2.04, 1.91, and –2.19 in the comparison between Group D
and Group A. In other words, these items were much harder for the two reading
disability groups. The RMSD was recalculated without the last three items, with
a result of a RMSD of .22 for Group C and .40 for Group D. The effect due to the
presence of the reading disability was recalculated: t = [(.22.13)/.11] = .82.
After
removing the last three items, the effect for reading disability was no longer
significant. The effect due to the readaloud accommodation was also
recalculated and was not significant (t = [(.40.22)/.11] = 1.64), although it
was close to the critical value of 1.68.
Math Test
Each of
the effects was negligible on the math test. The magnitude of estimation error
was .19. The RMSD for the effect due to the overall performance difference was
.20. As with the reading test, the effect for the overall performance difference
was not statistically significant. The presence of the performance difference
plus the reading disability resulted in a RMSD equal to .22, and a t equal to
.10. The result indicates that the presence of a reading disability does not
significantly change the construct being measured. In the absence of a
significant effect for the reading disability, it makes little sense to
determine whether the use of the readaloud accommodation significantly reduces
the item difficulty discrepancies. However, the effect was computed to determine
whether the presence of the readaloud accommodation made matters worse. The t
associated with the combined effect of the reading disability and the readaloud
accommodation was .53, not statistically significant. The magnitude of the
difference in model fit ranged from 605 for Group C to 614 for Group B. The fit
statistic suggests that neither disability status nor accommodation status, nor
the combination of the two, had an overall DIF effect.
DIF Item Results
The
third way in which the effect of each condition (i.e., the effect for ability
differences, the effect for disability status, and the effect for the
accommodation) was examined was to count the number of DIF items for each group.
An item was considered a DIF item if the ratio of the item difficulty difference
([bf
–br]/SE(bfbr)) exceeded 2.0. Because this ratio
is distributed as a tdistribution, a value of 2.0 indicates statistical
significance for a twotailed alpha equal to .05. Table 5 shows the number of
DIF items for each group on each test. Only one item was flagged as DIF for
Group B on the reading test, and none were flagged on the math test. Group C had
10 DIF items on the reading test, and only one DIF item on the math test.
Finally, Group D had 19 DIF items on the reading test and six DIF items on the
math test.
Table 5. The number of DIF items
Number of DIF items
Group
Reading
Math
B
1
0
C
10
1
D
19
6
Discussion
The
goal of this study was to ascertain whether the readaloud accommodation altered
the construct being measured by a reading test and a math test. Specifically, it
was argued that item characteristics for students with a reading disability
taking a math test and a reading test with the readaloud accommodation should
be the same as those for students without disabilities taking each test without
the accommodation, whereas the item characteristics for students with a reading
disability taking each test without the readaloud accommodation should differ
from the reference group.
Because
the students receiving the accommodation differed from the reference group on
three conditions—ability level, disability status, and use of an accommodation—a
direct comparison between the reference group and the group receiving the
readaloud accommodation would be confounded. It was necessary to include two
additional comparison groups—one that estimates the effect of the ability
difference, and one that estimates the effect of the combination of the ability
and disability status. Although we did not have the advantage of random
assignment to all conditions, by using three focal groups, the effect of the
readaloud accommodation could be estimated.
The
rootmeansquareddiscrepancy was used to index the overall item discrepancy. A
large RMSD suggests that the test items measure different constructs. The extent
to which the test items measured the same construct for the three conditions was
also evaluated by a count of the number of items that differed significantly
from the estimate in the nondisabled, nonaccommodated sample. These were
called DIF items.
Reading Test
The
results from the reading test indicated that item difficulty was substantially
different from the reference group for students with a reading disability taking
the test without an accommodation. In other words, in the absence of an
accommodation, reading comprehension test scores will not mean the same thing
for students with a reading disability as they do for other examinees. This type
of evidence suggests that some alteration to the regular test is necessary to
obtain comparable scores for students with and without a reading disability.
Providing certain accommodations to students with a reading disability is one
way to obtain comparable scores. A common accommodation is to read the test
passages and items to the students. The results of this study do not support
this approach. When the readaloud accommodation was provided to students with a
reading disability, the item difficulty differences actually increased. The RMSD
was two times greater for the students receiving the readaloud accommodation
than for students taking the test without it. Furthermore, the number of DIF
items nearly doubled from 10 items for the students taking the test without the
accommodation to 19 items for students taking the test with the readaloud
accommodation.
Reading
the reading test made a bad situation worse. Because students were not randomly
assigned to conditions, these results cannot be used to declare that reading the
reading test represents an invalid accommodation. However, this demonstrates
that the score for students receiving this accommodation in an actual testing
situation results in scores that do not mean the same thing as the scores for
the students without disabilities who do not receive accommodations.
Item
difficulty differences on the reading test were most pronounced on the last
three items. The item difficulty difference exceeded one standard deviation unit
on each of these three items. The direction of the difference indicated that
these items were much more difficult for the students with a reading disability,
whether they had the accommodation or not. When these items were removed, and
the RMSD was recomputed, the overall difference between the item difficulty
estimates for students with a reading disability taking the test without an
accommodation and the nondisabled reference group were no longer statistically
significant. Removing these items resulted in a nonstatistically significant
effect for the reading disability. This implies that, after removing the last
three items, the overall test scores for students with a reading disability
taking the test without an accommodation measure the same
latent trait as test scores for the nondisabled, nonaccommodated group. This
was not the result for the students receiving the readaloud accommodation. Even
with the last three items removed, the RMSD still indicated that test scores
were not a measure of the same latent trait as they were for students in the
reference group. The presence of the readaloud accommodation made things worse.
Math Test
Item
difficulty estimates for students with a reading disability taking the math test without an accommodation did not differ
significantly from the item difficulty estimates for the reference group. In
all, only one item was identified as a DIF item. Overall, the item difficulty
estimates were not significantly different from the reference group when
students with a reading disability took the math test using the readaloud
accommodation. These results suggest that test scores for the students with a
reading disability measured the same trait as they did in the reference group,
regardless of whether the student with a disability received the accommodation.
However, 6 of the 31 items were identified as DIF items. This finding indicates
that the readaloud accommodation altered the characteristics of some of the
items. Four of the six items were statistically easier for the students
receiving the accommodation than for the reference group after controlling for
the overall performance difference. If these items were word problems, it might
suggest that the accommodation removed extraneous difficulty due to the reading
load of the item. This study cannot verify this, but does point to the need to
attach DIF results to item characteristics.
The
results from the math test provide a mixed picture. On the one hand, the
findings indicate that the use of the readaloud accommodation did not
significantly alter item difficulty estimates. However, not reading the math
test to students with a reading disability did not result in poor measurement
either. Since the items seem to function the same for students with a reading
disability without an accommodation as they do for general education students,
one might wonder what the point would be in providing the readaloud
accommodation. Without the accommodation, only one item was flagged as DIF; with
the accommodation, six items were flagged as DIF. It appears that providing the
readaloud accommodation to students with a reading disability makes a good
situation bad.
One
possible explanation for why the readaloud accommodation resulted in more DIF
items is that administration of the readaloud accommodation may have been
flawed. Also, it may be that the readaloud accommodation was not uniformly
administered across schools. Nonuniform administration would introduce
additional random error to the test score, which could result in less stable
item difficulty estimates. One can easily imagine ways in which the readaloud
accommodation could differ from test proctor to test proctor. For instance, one
proctor may read with more pronounced voice inflexions and examinees could
construe them to represent a clue to the answer. This could also detract the
examinee from solving the problem by herself or himself, and instead focus on
unreliable cues. Other distractions may pose more of a problem when a proctor
reads test material than when a student reads it. If one becomes distracted
while reading, one could reread the material preceding the distraction. However,
one does not have this luxury when a proctor reads the material. Presentation of
the readaloud via recording with a welltrained reader may mitigate these
problems.
Another
potential problem can be attributed to group presentation of the readaloud
accommodation. In Missouri, the readaloud is administered by having a proctor
read the item and response choices to a small group of students or to individual
students. A student may ask the proctor to reread the problem. However, social
pressures may limit these requests even if the students or group of students did
not attend well to the initial reading. Because listening is a more passive
activity than reading, it is easy to imagine that a student may not be attending
well to the proctor, catching a student “offguard” will likely result in that
student missing the item. Furthermore, it is likely that students are aware of
the behavior of other students in the room. When the correct answer is read, a
student may watch how the other students react to each response option, and then
make their choice when the other students seem to be marking their choice.
There
also is the possibility that the readaloud accommodation was not given to the
students who actually needed it and was given to students who did not need it.
This possibility seems unlikely given that the analysis only included students
with a reading disability, however we have no basis for knowing whether some
students with a reading disability might need an accommodation while others with
the same disability might not. Presumably students with a reading disability
should benefit from the accommodation, whereas students without a reading
disability should not. It is possible that the identification of students with a
reading disability is inadequate.
There
is also the possibility that the readaloud accommodation is itself flawed, or
that it is simply unnecessary for the vast majority of students with reading
disabilities. Our findings indicate that the effect for the reading disability
was not significant on the math test; additionally, only one DIF item was
identified. These results indicate that the construct of the math test was not
affected by the presence of the disability; thus, there was no reason to
accommodate those students. Similarly, after removing the last three items on
the reading test, the effect due to the reading disability was not significant.
That is, reading comprehension test items tapped the same construct for students
with a reading disability as they did for the students without the reading
disability.
Our
findings indicate that either the readaloud accommodation was unnecessary for
students with reading disabilities taking a 3rd
grade reading comprehension test and a 4th
grade math test, or that only a subgroup of these students would benefit from
the accommodation. This study is just a first step in establishing the effect of
the readaloud accommodation on the construct validity of a reading and math
test. Strong conclusions about the validity of the readaloud accommodation
itself will require the accumulation of evidence from many studies, including
more analysis of extant data.
References
Anastasi, A. (1988). Psychological testing (6th
Ed). New York, NY: Macmillan Publishing Company.
Bielinski, J., Ysseldyke, J., Bolt, S., Friedebach, M., & Friedebach, J. (in
press). Prevalence of accommodations for students with disabilities
participating in a statewide testing program.
Diagnostique.
Elliott, S. N., Kratochwill, T. R., McKevitt, B, Schulte, A. G., Marquart, A. &
Mroch, A. (June, 1999). Experimental
analysis of the effects of testing accommodations on the scores of students with
and without disabilities: Midproject results. A paper presented at the
CCSSO LargeScale Assessment Conference, Snowbird, Utah, June, 1999.
Fuchs,
L. S., Fuchs, D., Eaton, S. B., Hamlett, C., & Karns, K. (2000). Supplementing
teacher judgments about test accommodations with objective data sources. School Psychology Review, 29 (1),
6585.
Fuchs,
L. S., Fuchs, D., Eaton, S. B., Hamlett, C., Binkley, E., & Crouch, R. (Fall,
2000). Using objective data sources to enhance teacher judgments about test
accommodations. Exceptional Children 67 (1), 6781.
Phillips, S. E. (1994). High stakes testing accommodations: Validity vs.
disabled rights. Applied Measurement in Education, 7 (2),
93120.
Pomplun, M., & Omar, H. M. (2000). Score comparability on a state mathematics
assessment across students with and without reading accommodations. Journal of Applied Psychology, 85 (1), 2129.
Thurlow, M., House, A., Boys, C., Scott, D., & Ysseldyke, J. (2000). State participation and accommodation policy
for students with disabilities: 1999 update (Synthesis Report 33).
Minneapolis, MN: National Center on Educational Outcomes.
Thurlow, M., & Weiner, D. (2000).
Nonapproved accommodations: Recommendations for use and reporting (Policy
Directions No. 11). Minneapolis, MN: University of Minnesota, National Center on
Educational Outcomes
Tindal,
G., Heath, B., Hollenbeck, K., Almond, P., & Harniss, M. (1998). Accommodating
students with disabilities on largescale tests: An experimental study. Exceptional Children, 64(4), 439451.
Tindal,
G., Helwig, R., & Hollenbeck, K. (1999). An update on test accommodations:
Perspectives of practice to policy.
Journal of Special Education Leadership, 12(2), 1120.
Tippets, E., & Michaels, H.(1997). Factor
structure invariance of accommodated and nonaccommodated performance
assessments. Paper presented at the National Council on Measurement in
Education annual meeting, Chicago.
Willingham, W. (1988). Testing handicapped
people: The validity issue. In Wainer, & Braun, (Ed). Hillsdale: NJ:
Lawrence Erlbaum.
Appendix A
Reading Test Adjusted Threshold Values
ITEM GROUP
A
C
D
B
R01
 3.574
 3.971
 4.155
 3.649
 0.272*  0.228* 
0.249*  0.207*
R02
 3.220
 3.429
 3.082
 3.312
 0.223* 
0.166*  0.172*  0.149*
R03
 0.614
 0.715
 0.646
 0.608
 0.128*  0.140* 
0.130*  0.120*
R04
 1.843
 2.132
 2.427
 1.818
 0.302*  0.288* 
0.293*  0.268*
R05
 1.511
 1.421
 1.085
 1.666
 0.126*  0.110* 
0.095*  0.098*
R06
 1.606
 1.913
 1.991
 1.647
 0.155*  0.155* 
0.146*  0.129*
R07
 1.378 
1.552 
1.504 
1.569
 0.138*  0.130* 
0.121*  0.116*
R08
 1.667
 1.636
 1.881
 1.850
 0.119*  0.111* 
0.111*  0.102*
R09
 0.582
 0.476
 0.646
 0.654
 0.077*  0.089* 
0.079*  0.069*
R10
 3.005
 3.305
 3.278
 3.104
 0.220*  0.181* 
0.192*  0.164*
R11
 0.194
 0.158
 0.811
 0.189
 0.092*  0.130* 
0.111*  0.115*
R12
 0.106
 0.098
 0.506
 0.001
 0.046*  0.076* 
0.057*  0.061*
R13
 0.799
 0.549
 0.571
 0.536
 0.130*  0.137* 
0.124*  0.113*
R14
 0.758
 0.591
 0.681
 0.652
 0.063*  0.154* 
0.140*  0.111*
R15
 0.990
 1.017
 1.267
 0.994
 0.093*  0.091* 
0.086*  0.077*
R16
 1.244
 1.203
 1.261
 1.153
 0.093*  0.086* 
0.078*  0.072*
R17
 0.020
 0.072
 0.212
 0.334
 0.121*  0.162* 
0.140*  0.147*
R18
 1.344
 1.058
 0.975
 1.235
 0.127*  0.112* 
0.104*  0.100*
R19
 0.421
 0.441
 1.036
 0.564
 0.049*  0.141* 
0.218*  0.105*
R20
 1.729
 1.994
 1.785
 1.843
 0.133*  0.124* 
0.116*  0.107*
R21
 0.278
 0.116
 0.538
 0.422
 0.055*  0.086* 
0.069*  0.089*
R22
 0.052
 0.378
 0.270
 0.119
 0.058*  0.080* 
0.075*  0.073*
R23
 0.111
 0.172
 0.413
 0.397
 0.085*  0.119* 
0.101*  0.090*
R24
 0.727
 1.062
 1.465
 0.816
 0.079*  0.084* 
0.085*  0.069*
R25
 0.073
 0.411
 0.366
 0.290
 0.088*  0.164* 
0.143*  0.117*
R26
 0.292
 0.418
 0.784  0.132
 0.089*  0.109* 
0.096*  0.095*
R27
 0.348
 0.491
 0.501
 0.315
 0.067*  0.082* 
0.077*  0.069*
R28
 0.698
 0.969
 0.980
 0.604
 0.100*  0.108* 
0.102*  0.092*
R29
 1.479
 1.490
 1.361
 1.431
 0.125*  0.112* 
0.103*  0.097*
R30
 0.596
 0.714
 0.928
 0.673
 0.112*  0.121* 
0.111*  0.101*
R31
 0.659
 0.865
 1.060
 0.797
 0.068*  0.073* 
0.070*  0.061*
R32
 0.932
 0.856
 1.263
 0.958
 0.083*  0.079* 
0.077*  0.068*
R33
 0.484
 0.738
 0.943
 0.467
 0.086*  0.096* 
0.091*  0.082*
R34
 0.407
 0.531
 0.333
 0.700
 0.063*  0.151* 
0.109*  0.120*
R35
 0.848
 0.565
 0.832
 0.703
 0.087*  0.090* 
0.080*  0.073*
R36
 2.020
 1.833
 1.950
 2.115
 0.286*  0.258* 
0.257*  0.245*
R37
 0.575
 0.578
 0.027
 0.492
 0.150*  0.158* 
0.160*  0.134*
R38
 0.129
 0.012
 0.044
 0.270
 0.087*  0.135* 
0.118*  0.094*
R39
 0.160
 0.985
 2.199
 0.054
 0.096*  0.245* 
0.510*  0.119*
R40
 2.051
 1.030
 0.137
 1.959
 0.187*  0.140* 
0.144*  0.135*
R41
 1.348
 0.035
 0.841
 1.355
 0.182*  0.187* 
0.241*  0.146*

*STANDARD ERROR
Appendix B
Math Test Adjusted Threshold
Values
ITEM
GROUP
A
C
D
B
M01  4.411
 4.030
 3.764
 3.996
 0.504*  0.418* 
0.413*  0.415*
M02
 2.524
 2.378
 2.006
 2.533
 0.323*  0.280* 
0.267*  0.283*
M03
 0.166
 0.095
 0.553
 0.167
 0.173*  0.200* 
0.201*  0.178*
M04
 3.904
 4.203
 4.172
 4.561
 0.377*  0.328* 
0.332*  0.339*
M05
 3.396
 3.998
 3.898
 3.631
 0.586* 
0.570*  0.566* 
0.554*
M06
 1.179
 1.105
 1.151
 1.113
 0.131*  0.124* 
0.111*  0.118*
M07
 0.852
 0.762
 0.653
 0.866
 0.073*  0.073* 
0.063*  0.068*
M08
 1.691
 1.572
 1.442
 1.866
 0.177*  0.160* 
0.144*  0.162*
M09
 0.611
 0.816
 1.029
 0.671
 0.101*  0.108* 
0.094*  0.102*
M10  0.021
 0.053
 0.044
 0.058
 0.093*  0.118* 
0.105*  0.109*
M11
 0.383
 0.313
 0.502
 0.420
 0.088*  0.134* 
0.123*  0.125*
M12
 1.431
 0.975
 0.924
 1.156
 0.122*  0.105* 
0.094*  0.100*
M13
 2.401
 2.718
 2.550
 2.771
 0.228*  0.211* 
0.205*  0.211*
M14
 2.710
 2.336
 2.628
 2.660
 0.201* 
0.160*  0.165* 
0.163*
M15
 1.569
 1.732
 1.866
 1.864
 0.143*  0.134* 
0.128*  0.129*
M16
 2.615
 2.440
 2.667
 2.785
 0.180*  0.158* 
0.162*  0.163*
M17
 1.911
 1.858
 1.856
 1.962
 0.166*  0.150* 
0.141*  0.146*
M18
 1.492
 1.623
 1.590
 1.401
 0.218*  0.205* 
0.194*  0.196*
M19  1.201
 1.260
 1.455
 1.050
 0.106*  0.100* 
0.094*  0.094*
M20
 1.527
 1.506
 1.471
 1.532
 0.120*  0.108* 
0.096*  0.101*
M21
 0.052
 0.101
 0.237
 0.072
 0.077*  0.100* 
0.081*  0.091*
M22
 0.542
 0.834
 0.967
 0.733
 0.124*  0.130* 
0.118*  0.125*
M23
 0.255
 0.240
 0.131
 0.256
 0.088*  0.104* 
0.092*  0.096*
M24
 0.122
 0.086
 0.139
 0.211
 0.049*  0.068* 
0.056*  0.069*
M25
 0.006
 0.188
 0.125
 0.199
 0.066*  0.084* 
0.073*  0.087*
M26
 1.235
 1.087
 1.163
 1.218
 0.092*  0.084* 
0.074*  0.079*
M27
 0.470
 0.566
 0.604
 0.364
 0.065*  0.073* 
0.061*  0.072*
M28
 1.287
 1.123
 1.407
 1.243
 0.131*  0.117* 
0.109*  0.112*
M29
 0.466
 0.491
 0.597
 0.765
 0.057*  0.115* 
0.105*  0.119*
M30
 0.755
 0.772
 0.885
 0.546
 0.075*  0.078* 
0.067*  0.075*
M31
 1.143
 0.914
 0.885
 0.971
 0.163*  0.154* 
0.139*  0.148*
M32
 0.230
 0.276
 0.133
 0.237
 0.106*  0.145* 
0.112*  0.132*

*STANDARD ERROR
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