Rasch Analysis of the Ocular Surface Disease
Bradley E. Dougherty,1Jason J. Nichols,2and Kelly K. Nichols2
PURPOSE. The Ocular Surface Disease Index (OSDI) is a 12-item
scale for the assessment of symptoms related to dry eye disease
and their effect on vision. Its reliability and validity have been
investigated within the classical test theory framework and, more
was to more completely investigate the functioning of its re-
sponse category structure, the validity of its three subscales, and
the unidimensionality of the latent construct it is intended to
METHODS. Responses to the OSDI from 172 females participating
in the Dry Eye in Postmenopause (DEiM) study who had previ-
ously been diagnosed with dry eye or reported significant ocular
irritation and dryness were analyzed. Response category structure
and item fit statistics were evaluated for assessment of model fit.
Person separation statistics were used to examine the validity of
the subscales. Unidimensionality was assessed by principal com-
ponent analysis (PCA) of model residuals.
RESULTS. The recommended five-category response structure re-
sulted in disordered response thresholds. A four-category struc-
ture resulted in ordered thresholds. Item infit statistics were ac-
ceptable for all 12 items. Person separation with this category
structure was adequate, with a person separation index of 2.16.
None of the three subscales demonstrated adequate person sep-
aration. PCA showed one other significant factor onto which the
three environmental items loaded significantly.
CONCLUSIONS. All items demonstrated acceptable fit to the model
after collapsing categories to order the response thresholds. The
original subscales did not prove valid, and there is some evidence
of multidimensionality and poor targeting. (Invest Ophthalmol
Vis Sci. 2011;52:8630–8635) DOI:10.1167/iovs.11-8027
ular surface disease severity in dry eye research. It has been used
in a variety of studies, both in the assessment of the severity of dry
eye associated with certain conditions1–3and in the investigation
of the efficacy of dry eye treatments.4–7The instrument, intro-
duced in 1997 by the Outcomes Research Group (Allergan Inc.,
Irvine, CA),8consists of 12 items that assess symptoms, functional
limitations, and environmental factors related to dry eye. Each
he Ocular Surface Disease Index (OSDI) is perhaps the most
frequently used survey instrument for the assessment of oc-
item has the same five-category Likert-type response option, and
each of the three subscales has its own question type.
Initial investigations of the reliability of the OSDI were con-
ducted using Classical Test Theory methods, that is, using the
Cronbach’s ? statistic to assess the internal consistency of the
items.9There are several excellent references on Classical Test
Theory and its use in survey research.10,11Schiffman et al.9found
that Cronbach’s ? for the OSDI was 0.92, and a factor analysis
revealed three subscales (symptoms, environmental triggers, and
vision-related function). They reported acceptable test–retest re-
peatability, but found that OSDI scores did not correlate particu-
larly well with clinical tests for dry eye. Specifically, correlations
for all subjects between OSDI score and tear break-up time,
Schirmer’s test, lissamine green, and fluorescein staining in the
worse eye ranged from ?0.21 to ?0.19 and none of the correla-
tions was statistically significant.
The use of Classical Test Theory to score survey instruments
and evaluate their reliability has been criticized for several rea-
sons. One of these is the treatment of Likert-type survey data as
continuous, rather than ordinal. Another is the assignment of
equal weight to each survey item in the calculation of an overall
score, when one could imagine situations in which items may
require different levels of the underlying trait for endorsement.
For these reasons, the common method of generating an overall
score from an instrument—summing and averaging the ordinal-
level responses—is open to criticism. Also, the approach of using
Cronbach’s ? to assess reliability does not provide information
regarding the behavior of individual survey items, only the instru-
ment as a whole.
A family of models known collectively as Item Response The-
ory (IRT) provides an alternative approach to the scoring and
evaluation of survey instruments. The models have roots in edu-
cation and aptitude testing, and seek to quantify the amount of
some latent trait using survey responses. Rasch analysis, also
sometimes referred to as a one-parameter logistic model, is often
considered as the simplest of the IRT models.12,13Rasch analysis
was initially developed for educational testing, but is now fre-
quently used in healthcare research to evaluate and score survey
instruments.14–18It provides interval-level data from survey re-
sponses, including estimates of the amount of the latent trait
displayed by each subject (“person measure”) and the amount of
the trait necessary to respond in a certain way to each item (“item
measure”).12Moreover, Rasch analysis also provides item fit sta-
tistics that indicate whether the individual item is contributing to
the measurement of the latent trait. There are excellent descrip-
tions of Rasch analysis and its use with survey instruments pub-
Other IRT models include parameters in addition to the item
difficulty parameter used in Rasch analysis. For instance, some
models include an item discrimination parameter that allows for
differences in the slope of the curve describing response proba-
bilities for individual items.22Rasch models are restrictive in the
sense that data that do not conform to the model are not consid-
ered to be consistent with measurement, and the curves describ-
ing response probabilities are not free to vary between items.
From the1The Ohio State University College of Optometry, Co-
lumbus, Ohio; and the2University of Houston College of Optometry,
Supported in part by National Eye Institute of the National Insti-
tutes of Health Grants EY017951 (JJN, KKN), EY015519 (KKN, JJN),
and 5T32EY013359 (BED).
Submitted for publication June 10, 2011; revised September 8,
2011; accepted September 16, 2011.
Disclosure: B.E. Dougherty, None; J.J. Nichols, None; K.K.
Corresponding author: Kelly K. Nichols, University of Houston
College of Optometry, 505 J. Davis Armistead Building, Houston, TX
Clinical and Epidemiologic Research
Investigative Ophthalmology & Visual Science, November 2011, Vol. 52, No. 12
Copyright 2011 The Association for Research in Vision and Ophthalmology, Inc.
Other IRT models seek to describe the data as best as possible
using extra parameters such as item discrimination. Massof23
published a study in which he compared a Rasch model to a
two-parameter logistic IRT model (the Muraki model) using data
from visual functioning questionnaires. He demonstrated that the
item discrimination parameter of the Muraki model was inversely
proportional to the item fit statistics of the Rasch model.
There has been some recent work in the area of evaluation of
dry eye survey instruments with Rasch analysis. Gothwal et al.14
examined the measurement properties of the McMonnies ques-
tionnaire using Rasch analysis. They found that person separation
was inadequate for discriminating between more than two strata
of dry eye severity and, therefore, the McMonnies questionnaire
did not function as a valid measure to discriminate across disease
Johnson and Murphy24developed the Ocular Comfort Index
(OCI) to measure ocular surface disease symptoms using Rasch
analysis. The instrument they developed has 12 items and a
seven-category response structure. Person separation was good
and all 12 of the final items had adequate Rasch fit statistics.
Simpson et al.25evaluated the Dry Eye Questionnaire, the
McMonnies questionnaire, and the OSDI. One purpose of this
study was to evaluate the Rasch item fit statistics of the instru-
ments and use them to determine whether the surveys were
fit statistics within the acceptable range. Other aspects of the
analysis, such as the functioning of the category structure and
person separation statistics, were not reported.
Pesudovs and Noble26evaluated a single-item faces scale for
measuring pain associated with severe ocular surface disease.
They applied Rasch analysis to refine the category structure of the
instrument. The study also used the scale to demonstrate the
potential of Rasch analysis to increase sensitivity to changes after
treatment for ocular surface disease, finding an increased effect
size with Rasch analysis compared with conventional raw
In light of this, we hypothesize that the application of Rasch
analysis to responses to the OSDI from patients with dry eye
disease might be beneficial in further understanding its psycho-
metric properties. Thus, the purpose of this study was to investi-
gate the OSDI using Rasch analysis in a sample of females 50 years
of age and older who were participating in a study of dry eye in
postmenopause and who had been previously diagnosed with dry
eye or reported significant ocular dryness and irritation.
The OSDI was administered to female participants in the Dry Eye in
Postmenopause study at the College of Optometry at The Ohio State
University. The OSDI scores of participants were included in the analysis
if at least one of two criteria were met. The first of these was that the
participant reported having been previously diagnosed with dry eye by an
eye care provider. The second was that the participant answered “often”
or “constantly” to both of the following questions: “How often do you
experience eye dryness?” and “How often do your eyes feel irritated?”
These questions were previously used by Schaumberg and colleagues27–29
for classification of patients by dry eye status in large-scale epidemiologic
studies of the prevalence of dry eye. The mean age (?SD) of participants
was 63 ? 8 years. Potential participants were excluded from the study if
were taking eye drops for an ocular condition other than dry eye, had a
history of any eye surgery other than secondary membrane removal after
cataract extraction in the past year, or reported other significant ocular
pathology. Informed consent was obtained from all participants, in accor-
dance with The Health Insurance Portability and Accountability Act of
1996 (HIPAA) regulations and the Declaration of Helsinki.
The OSDI response structure contains five options that relate to the
frequency of the effects of ocular surface disease: “none of the time,”
“some of the time,” “half of the time,” “most of the time,” and “all the
time.” There are three question types: “Have you experienced any of the
following during the last week?” (items 1–5); “Have problems with your
eyes limited you in performing any of the following during the last week?”
(items 6–9); and “Have your eyes felt uncomfortable in any of the follow-
ing situations during the last week?” (items 10–12). Rasch analysis was
performed with a commercial software knowledgebase (WINSTEPS ver-
sion 3.69; Winsteps, Chicago, IL), using a three-level Andrich rating scale
model.30For the response structure to be valid, the category thresholds,
or the point on the logit scale of ability at which a subject is equally likely
to choose between two adjacent categories, should be ordered. That is,
these threshold person measures should increase in order with the cate-
gories so that subjects with increasing amounts of the trait of interest have
olds proved disordered, categories were combined to obtain ordered
thresholds. Once ordered category thresholds were established, instru-
ment and item-level statistics were analyzed. Published guidelines regard-
ing acceptable item fit and other Rasch analysis statistics were used to
guide the analysis.20
Item infit mean square statistics were used to determine whether
individual items provided useful information for measurement of ocu-
lar surface disease severity. The infit mean square is an information-
weighted fit statistic that compares observed data with model expec-
tations. Items with infit values outside of 0.7–1.3 were eliminated one
at a time, beginning with the most misfitting item, and the analysis was
repeated until no items misfit.
The ability of the instrument to discriminate between participants
was assessed using the person separation statistic. Person separation is
a ratio of the variance explained by the measures to the total variance
(including error variance).12A value of 2.0 was considered the mini-
mum acceptable value and corresponds to the ability to differentiate
between three levels of a trait. Person separation was also used to
evaluate the validity of the three subscales, with the same minimum
If an instrument is used to report a single measure, it should
assess only one latent trait. Principal component analysis (PCA) of
Rasch residuals (performed using WINSTEPS version 3.69) was used
to assess unidimensionality. If an instrument is unidimensional, then
PCA of the model residuals should reveal no structure in those
residuals.32Significant loading onto other factors in the analysis is
indicative of multidimensionality. Factors with eigenvalues (an in-
dicator of the proportion of the total variation explained by an
individual factor) ?2.0 were considered to be evidence of signifi-
Response Category Functioning
Category thresholds with the OSDI five-category response struc-
ture were shown to be disordered (Fig. 1). A four-category re-
sponse structure, in which the categories “half of the time” and
“most of the time” were combined, had ordered thresholds and
fairly equal widths over which each category was the most likely
response, which is desirable26(Fig. 2). A different four-category
structure in which the categories “all the time” and “most of the
time” were combined was also tested, but it did not result in
ordered thresholds. The four-category structure that combines
“half of the time” and “most of the time” was used for the rest of
Of the 172 female participants who completed the OSDI, 7
responded “none of the time” to all 12 items. Data from
IOVS, November 2011, Vol. 52, No. 12
Rasch Analysis of the OSDI 8631
these subjects were not included in the analysis, leaving 165
subjects who were included. When the four-category re-
sponse structure was used, all 12 items had infit mean
square statistics within the acceptable range of 0.7 to 1.3.
The item measures and infit statistics are shown in Table 1.
The person separation index for the 12-item instrument was
2.16, which indicates that the OSDI can adequately discrimi-
nate between patients.
The person separation indices for each of the three subscales
are shown in Table 2. None of the subscales met the criterion
of a person separation index of at least 2.0, which indicates
that none of the subscales adequately differentiated between
different levels of the targeted constructs.
Principal component analysis of the standardized model resid-
uals indicated that there was one additional factor onto which
items were loading significantly. The first contrast had an
ment show ordered thresholds. Curves for items 1–5, 6–9, and 10–12
are shown in (a), (b), and (c), respectively. Blue: none of the time; red:
some of the time; green: half of the time or most of the time; purple:
all the time.
Category probability curves for the four-category instru-
ment showing underutilization of category 3 and disordered thresh-
olds. Curves for items 1–5, 6–9, and 10–12 are shown in (a), (b), and
(c), respectively. Blue: none of the time; red: some of the time; green:
half of the time; purple: most of the time; light blue: all the time.
Category probability curves for the five-category instru-
8632 Dougherty et al.
IOVS, November 2011, Vol. 52, No. 12
eigenvalue of 2.6 (11.1% of the total variance), which is more
than can be attributed to random data. Items that loaded
significantly (?0.4) onto this factor included the three envi-
ronmental triggers items (“windy conditions,” “low humidity,”
and “air conditioned”) and one other item (“gritty”). The sec-
ond contrast had an eigenvalue of 1.6, or 6.6% of the total
Because of the evidence of multidimensionality, we inves-
tigated whether a shorter instrument that does not contain the
environmental triggers items might function as a valid instru-
ment on its own. To investigate this question, we performed an
analysis using items 1 to 9 with only a two-level Andrich rating
scale model. The person separation index for this 9-item in-
strument was 1.82, which does not meet the criteria for ade-
Our analyses indicate that the response category structure
recommended for the OSDI responses currently is not ideal
and can be optimized using Rasch analysis. We found that the
categories should be collapsed to get them to work properly.
Specifically, we found that combining the categories “half of
the time” and “most of the time” was necessary. Once this
change to the category structure was made, the categories
Regarding the fit of the items to the Rasch model, we found
results similar to Simpson et al.25The fit of the items was
generally good, with fit statistics falling within the recom-
mended range of Pesudovs et al.20for all items.
The person separation index for the OSDI was acceptable,
at 2.16. This demonstrates that the full 12-item OSDI is a useful
instrument for discriminating between people with varying
levels of ocular surface disease. We also found that none of the
three subscales had adequate person separation indices to
function acceptably on its own.
Unidimensionality and Rasch analysis in general have been
previously described for the OSDI only once, by Simpson et
al.25Our study explored additional aspects of the Rasch anal-
ysis and explored unidimensionality in another way. We found
that the instrument does not meet the standard of unidimen-
sionality when tested using PCA of the model residuals. This is
an important requirement for the use of summary scoring, in
that a summary score implies that all the items assess the same
Previous analyses of the unidimensionality of the OSDI and
the Ocular Comfort Index (OCI) were performed using item fit
statistics but not PCA. Although the fit of the items to the
model is one indicator of the unidimensionality of an instru-
ment, PCA is another useful tool for the detection of multiple
dimensions and may reveal evidence of multidimensionality
not detected with item fit statistics alone.21Our PCA indicates
that there is evidence of multidimensionality in the OSDI.
Specifically, the first contrast of the analysis showed unex-
plained variance of 2.6 eigenvalue units. Additionally, an anal-
ysis of the remaining nine items of the OSDI (not including the
environmental triggers items) showed that they do not have
adequate person separation to function as a separate scale.
The presence of multidimensionality in survey instruments
is problematic, in that if more than one latent trait is being
assessed by an instrument it becomes impossible to interpret a
single score from that instrument as a measure of any one trait.
We are not aware of any survey instrument specific to ocular
surface disease that has been demonstrated to be unidimen-
sional using PCA. Because dry eye is a multifactorial disease,
investigators may have a desire to investigate the multiple
aspects of the disease, such as symptoms and effects on visual
functioning. One approach to managing this problem is to use
multiple subscales, each of which is capable of assessing a
single trait of interest in a valid manner. This would require
subscales that have adequate discriminative ability, have items
with acceptable fit statistics, and that are unidimensional. This
approach would also require that scores from individual sub-
scales, each of which is an indicator of a distinct latent trait
related to ocular surface disease, not be combined into a single
score for a larger instrument.
The need for more work in the area of patient-reported
outcome measures in the area of dry eye and ocular surface
disease was recently highlighted in the report on meibomian
gland dysfunction from the International Workshop on Meibo-
mian Gland Dysfunction.34,35Future work in instrument devel-
opment should seek to create unidimensional scales, rather
than multidimensional scales that capture multiple aspects of
the disease and report a single, difficult to interpret, score.
The targeting of the OSDI—how well the difficulty of the
items matches the ability of the subjects taking the survey—
was not ideal. This is shown in Figure 3, which indicates that
many of the participants had an ability level higher than the
level of most or all the items contained in the instrument. The
average person measure for the participants in this study, all of
whom reported previous dry eye diagnoses or significant ocu-
lar irritation, was ?1.51. Ideally, the average item measure (set
to 0 in the analysis) would be close to the average person
measure and the range of ability covered by the set of items
TABLE 1. Item Measures and Infit Mean Square Fit Statistics for the
12 OSDI Items Using a Four-Category Response Structure, Collapsing
“Half of the Time” and “Most of the Time”
Item Item Measure (SE)Infit Mean Square
1. Light Sensitivity
4. Blurred Vision
5. Poor Vision
7. Driving at Night
11. Low Humidity
12. Air Conditioning
TABLE 2. Rasch Summary Statistics for the OSDI and Its Subscales Using a Four-Category Response Structure, Collapsing “Half of the Time” and
“Most of the Time”
Mean Rasch Person
Mean Rasch Item
Mean Item Infit
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Rasch Analysis of the OSDI8633
would be wide enough to adequately assess all the subjects.
Johnson and Murphy24reported similar targeting for the OCI.
However, in that study participants had not necessarily been
diagnosed with dry eye or reported significant symptoms as
had participants in this study, and it is important to consider
differences in the participants when considering the targeting
All the participants in this study were female and ?50 years
of age, thus limiting the ability to analyze whether there is
differential functioning of items based on age or sex. However,
dry eye and ocular surface disease are important concerns for
postmenopausal females and information regarding the useful-
ness of survey instruments in this population is of great impor-
tance. Future studies should investigate differential item func-
tioning. This would necessitate the inclusion of patients with a
wider age distribution than that of the present study and of
both males and females.
In conclusion, all items of the Ocular Surface Disease Index
showed acceptable fit to a Rasch measurement model and
adequate between-patient discrimination. However, there is
evidence from principal components analysis that it is not
unidimensional. Moreover, it is not ideally targeted for patients
with dry eye disease. Future studies in patient-reported out-
come measures for dry eye and ocular surface disease should
address these issues.
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Rasch Analysis of the OSDI8635