Psychology
2013. Vol.4, No.6, 526-534
Published Online June 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.46075
526
Factor Structure and Psychometric Properties of the Persian
Disgust Scale-Revised: Examination of Specificity to Symptoms
of Obsessive-Compulsive Disorder
Giti Shams1*, Elham Foroughi2, Melanie W. Moretz3, Bunmi O. Olatunji4
1Roozbeh Hospital, Department of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
2Department of Psychology, School of Behavioral Science, The University of Melbourne,
Melbourne, Australia
3Ferkauf Graduate School of Psychology, Yeshiva University, New York, USA
4Department of Psychology, Vanderbilt University, Nashville, USA
Received March 9th, 2013; revised April 13th, 2013; accepted May 11th, 2013
Copyright © 2013 Giti Shams et al. This is an open access article distributed under the Creative Commons At-
tribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
Although a growing body of research has implicated disgust in the etiology of a variety of anxiety disor-
ders, there remains a paucity of research examining this phenomenon across different cultures. The pre-
sent study examined the factor structure and psychometric properties of a newly adapted Persian Disgust
Scale-Revised (PDS-R). A large sample (n = 374) of Iranian students completed the PDS-R and other
symptom measures of psychopathology including obsessive-compulsive disorder (OCD). Results showed
good internal consistency and test-retest reliability of the PDS-R. Confirmatory factor analysis found
support for two- and three-factor models of the PDS-R. However, examination of internal consistency es-
timates suggests that a two-factor model of contagion disgust and animal-reminder disgust may be more
parsimonious. The PDS-R total and subscale scores displayed theoretically consistent patterns of correla-
tions with symptom measures of psychopathology. Structural equation modeling also revealed that latent
disgust sensitivity, defined by the contagion disgust and animal-reminder disgust subscales of the PDS-R,
was significantly associated with latent symptoms of contamination and non-contamination-based OCD
when controlling for latent negative affect. The implications of these findings for the cross-cultural as-
sessment of disgust in the context of anxiety related pathology are discussed.
Keywords: Persian Disgust; Obsessive-Compulsive Disorder; Contagion
Introduction
The traditional definition of disgust is linked with food rejec-
tion (e.g. Angyal, 1941; Darwin, 1872/1965; Ekman & Friesen,
1975; Tomkins, 1963), revulsion at the prospect of oral incur-
poration of an offensive object (Rozin & Fallon, 1987). How-
ever, stimuli that elicit disgust varies widely (Rozin, Haidt, &
McCauley, 2000). Accordingly, it is of central importance that
measures of individual differences in disgust capture the di-
verse range of disgust elicitors. However, the first self-report
measure of disgust, the Disgust Questionnaire (DQ, Rozin,
Fallon, & Mandell, 1984), was developed to measure only con-
cerns about food contamination. Appreciation of the diverse
range of disgust led to the development of the Disgust Scale
(Haidt et al., 1994), a more comprehensive measure of the wide
range of disgust elicitors. The DS consists of 32 items and as-
sesses eight domains of disgust: 1) food that has spoiled; 2)
animals that are slimy or live in dirty conditions; 3) body prod-
ucts including body odors, feces, mucus, etc.; 4) body envelope
violations, or mutilation of the body; 5) death and dead bodies,
6) culturally deviant sexual behavior; 7) hygiene violations, and
8) sympathetic magic (improbable contamination).
Although the DS has been instrumental in furthering current
understanding on the nature and function of disgust, the scale is
not without limitations, particularly considering the poor inter-
nal consistency of the individual subscales (Haidt et al., 1994;
Tolin et al., 2006). More recently, Olatunji and colleagues
(2007) refined the DS by removing items that displayed poor
psychometric properties. This re-analysis revealed that the DS
assesses 3 dimensions of disgust: Core Disgust, Animal Re-
minder Disgust, and Contamination-Based Disgust. Importantly,
the reliability of the three theoretically driven subscales were
much higher than that of the original eight subscales. Structural
modeling of this Disgust Scale-Revised (DS-R) also provided
support for the specificity of the 3-factor model, as Core Dis-
gust and Contamination-Based Disgust were significantly pre-
dictive of obsessive-compulsive disorder OCD) concerns,
whereas Animal Reminder Disgust was not. Furthermore, re-
sults from a clinical sample indicated that patients with OCD
washing concerns scored significantly higher than patients with
OCD without washing concerns on both Core Disgust and
Contamination-Based Disgust, but not on Animal Reminder
Disgust.
*Corresponding author.

G. SHAMS ET AL.
The comprehensive refinement by Olatunji and colleagues
(2007) has addressed many of the psychometric limitations of
the DS. Although disgust has been conceptualized as a basic
emotion that is observed across different cultures (Haidt, Rozin,
MacCauley, & Imada, 1997) however, the extent to which these
findings extend across cultures remains unclear. Recently, Ola-
tunji and colleagues (2009) evaluated the factor structure of the
DS-R in Australia, Brazil, Germany, Italy, Japan, the Nether-
lands, Sweden, and the United States. Support was found for
the three-factor solution consisting of core disgust, animal-
reminder disgust, and contamination disgusts all countries ex-
cept the Netherlands. The present study extends research on the
cross-cultural assessment of disgust by examining the factor
structure and psychometric properties of a newly developed
Persian DS-R (PDS-R). It was predicted that the PDS-R would
yield three replicable factors consisting of core disgust, con-
tamination disgust, and animal-reminder disgust. This study
also examined the reliability and validity of the PDS-R in rela-
tion to OCD symptoms
Research has also shown that high disgust propensity (i.e.,
the frequency or ease with which one generally responds with
disgust) is associated with various psychopathological condi-
tions (Olatunji & Sawchuk, 2005). There has been a surge of
research interest on the role of disgust in the etiology of various
anxiety disorders (Olatunji & Sawchuck, 2005; Woody &
Teachman, 2000), including contamination-based obsessive-
compulsive disorder (OCD). Fear of contamination is one of the
most common themes associated with OCD (Stekette, Grayson,
Foa, 1985; Rasmussen & Tsuang, 1992).
Studies have reported significant associations between dis-
gust proneness and a range of OCD symptoms such as hoarding,
neutralizing, ordering and religious obsessions. However, re-
search has consistently demonstrated a stronger positive asso-
ciation between self-report measures of disgust propensity and
the contamination subtype (Mancini et al., 2001; Moretz &
McKay, 2008; Olatunji et al., 2004). For example, Olatunji et al
(2004) found that scores on self-report measures of disgust
propensity accounted for 43% of the variance in scores on the
contamination subscale of the Padua Inventory (Burns et al.,
1996). Moreover, studies have found that the relationship be-
tween disgust and contamination fear remains when controlling
for negative affect and depression (e.g., Moretz & McKay,
2008; Olatunji et al., 2007; Tolin, Woods, & Abramowitz,
2006). In a more recent study, Olatunji (2010) found that
changes in disgust sensitivity levels (the perceived negative
impact of experiencing disgust) over a 12-week period pre-
dicted change in symptoms of contamination-based OCD, even
after controlling for age, gender, and change in negative affect.
Behavioral research has also implicated disgust in contamina-
tion-based OCD. For example, Deacon and Olatunji (2007)
found that disgust levels significantly predicted behavioral
avoidance of sources of contamination even when controlling
for negative Affect. Hence, evidence from both self-report and
behavioral tasks suggests that the tendency to experience dis-
gust may be a risk factor for the development of contamina-
tion-based OCD (Olatunji, Cisler, McKay, & Phillips, 2010).
The present study also investigated the association between DS
and OCD symptoms as measured by self-report instruments,
based on previous findings it was predicted that disgust prone-
ness would remain significantly associated with OCD symp-
toms after controlling for negative Affect.
Methods
Participants
Participants were 374 students with a mean age of 20.54
years (SD = 2.61). The majority of the sample consisted of
women (77%), and all participants were Muslim. Participation
was voluntarily and no payment or course credits were offered.
One hundred and ninety six (124 women) of the Participants
completed the self-report measures described below approxi-
mately 1 month after their initial administration.
Measures
All participants completed the Farsi version of the following
measures. The measures in this study (with the exception of the
Disgust Scale-Revised) were already translated into Persian and
were reported to show sound psychometric properties compara-
ble to those obtained in Western samples.
The Disgust Scale-Revised (DS-R; Haidt,., 1994; modified
by Olatunji et al., 2007) is a 25-item self-report measure of
disgust sensitivity. Respondents answer each item along a
5-point Likert-type scale. Lexical anchors range from 0 =
(Strongly disagree) to 4 = (Strongly agree). Olatunji et al. (2007)
found evidence for three factors of core, animal-reminder, and
contamination disgust.
The Obsessive-Compulsive Inventory-Revised (OCI-R; Foa
et al., 2002) is an 18-item questionnaire based on the earlier
84-item OCI (Foa, Kozak, Salkovskis, Coles, & Amir, 1998).
Participants rate the extent to which they are bothered or dis-
tressed by OCD symptoms in the past month on a 5-point
Likert-type scale ranging from 0 (not at all) to 4 (extremely).
The OCI-R assesses symptoms of OCD across six domains
including: washing, checking/doubting, obsessing, mental neu-
tralizing, ordering, and hoarding.
The Padua Inventory-revised (PI; Burns et al., 1996) con-
tamination subscale is a 10-item self report instrument that
measures as individual’s aversion towards contamination (e.g.,
I feel my hands are dirty when I touch money”. Individuals
respond to each item on a 5-point Likert scale indicating the
degree to which they would be disturbed by the situation de-
scribed in the items (0 = “not at all”, 4 = “very much”). The
total score is computed by summing the 10 items. The complete
PI has adequate psychometric properties and the contamination
subscale has high internal consistency (alpha = .085; Burns et
al., 1996). The PI contamination subscale correlates highly with
other measures of contamination fear (Burns et al., 1996; Thor-
darson et al., 2004).
The Vancouver Obsessional Compulsive Inventory (VOCI;
Thordarson, Radomsky, Rachman, Shafran, Sawchuk, & Hak-
stian, 2004). The VOCI-Contamination Subscale is a 12-item
subscale of the VOCI questionnaire that assesses fear of physic-
cal contamination. Items involve direct physical contact with a
contaminant, (e.g., I feel very dirty after touching money),
amount of time spent removing physical contaminants (e.g., I
spend far too much time washing my hands), and concerns
about germs and disease (e.g., I am afraid to use even well kept
public toilets because I am so concerned about germs). Excel-
lent internal consistency and convergent and divergent validity
have been demonstrated for the overall VOCI scale.
The State-Trait Anxiety Inventory (STAI-S; Speilberger et
al., 1983) is a 20- item measure of state anxiety or how anxious
the participant feels “right now”. State-Trait Anxiety Inven-

G. SHAMS ET AL.
528
tory—Trait Scale (STAI-T): The STAI-T has high reliability
and validity.
The Beck Depression Inventory-II (BDI-II; Beck, Steer, &
Garbin, M.G. 1988) is a 21-item, self-report measure of depres-
sive symptoms that was developed to adjust for alterations in
the DSM-IV diagnostic criteria. Participants indicate how fre-
quently they have experienced each of the 21 symptoms over
the past two weeks on a 4-point Likert scale from “never” to 4
= “all the time.”
The Penn State Worry Questionnaire (PSWQ;Meyer, Miller,
Metzger, & Borkovec, 1990) is a 16-item self-report inventory
that assesses the individual’s tendency to experience worry.
The items focus on the excessiveness, duration and uncontrol-
lability of worry and its related distress. Each item is rated on a
5-point scale 1 = “not at all typical of me” to 5 = “very typical
of me”.
Development of the PDS-R
The English version of the DS-R was translated to Persian by
the first author in Iran (forward translation, Step 1). The Persian
version of the DS-R was compared to the original English ver-
sion of the DS-R by two clinical psychologists and one psy-
chiatrist (Step 2). Based on feedback received from compari-
sons of the Persian and English version, minor changes were
made. A small group of volunteers (n = 20) were given a copy
the Persian translation of the DS-R; the volunteers were asked
to comment on how well they understood its content. Addi-
tional minor changes were made based on their suggestions.
The modified version of the Persian DS-R was then back trans-
lated to English.
In order to make the scale culturally relevant to the Iranian
population, the following changes were implemented. The
original item 5: “I would go out of my way to avoid walking
through a graveyard” was not appropriate for this population in
Iran, graveyards are located outside of the city and inhabited
areas and one would have to make special effort to get there.
Thus, this item was changed to “I feel nauseated being in a
graveyard, therefore I avoid going there” in accordance to Ira-
nian domestic culture. The original item 26, “As part of a sex
education class, you are required to inflate a new unlubricated
condom, using your mouth”, was also inconsistent with cultural
norms, as sex classes are not common practice and are rarely
held in Iran. Therefore this item was replaced with “While eat-
ing at a restaurant, you find a strand of hair in your food”. The
original item 24”, you accidentally touch the ashes of a person
who has been cremated” was slightly modified to reference
only a “dead body” as cremation of the body is forbidden in
Islam. “Monkey” in item 1 was also replaced with “‘cat” given
that monkeys are not a domestic animal in Iran. “Science class”
in item 2 was also replaced with “laboratory”, which is not
specific to students only.
Data Analysis Overview
LISREL 8.80 (Jöreskog & Sörbom, 2006) was used to ana-
lyze the data. The factor structure of the Persian DS-R was
determined using LISREL confirmatory factor analytic (CFA)
techniques. To determine the best fitting model for the sample,
three competing models of interest were estimated. A LISREL
system file containing the polychoric correlation matrix served
as the input data. The models were tested with the asymptotic
covariance matrix (ACM) as a weight using the Weighted Least
Squares (WLS) method of estimation. The WLS estimation is
the only method of estimation that produces an asymptotically
correct chi-square test of model fit with ordinal indicators
(Byrne, 1998).
The first indicator for each latent variable was constrained to
a factor loading of 1 to serve as a reference variable and to set
the metric. The following criteria were used to test the models’
fit: the root-mean-square error of approximation (RMSEA),
with values less than .08 indicating reasonable errors of ap-
proximation in the population and values less than .05 indica-
tive of a good fit (Byrne, 1998; McDonald & Ho, 2002); the
90% confidence interval for the RMSEA, with a wide confi-
dence interval indicating an imprecise estimate of the degree of
fit in the population (MacCallum, Browne, & Sugawara, 1996);
the comparative fit index (CFI), with values greater than .90
indicative of an acceptable fit (Hu & Bentler, 1999); and the
adjusted goodness of fit index (AGFI), with values greater
than .95 indicative of a good fit (Byrne, 1998). The fit of com-
peting models was tested by the chi-square difference test
(CSDT) and comparison of the Akaike Information Criterion
(AIC; Akaike, 1987). A no significant difference in χ2 between
two competing models suggests the model has not lost its
goodness of fit with additional imposed parameters .The AIC
criterion is frequently used in model selection. When compar-
ing two competing models, the model with the lowest AIC is
considered the preferred model (Burnham & Anderson, 2002).
This study compared two different structural equation mod-
els to examine the relationship between Disgust Sensitivity,
Negative Affect, and OCD Symptoms. SEM offers the advan-
tage of estimating and removing measurement error in the
models, leaving only common variance among factors (Ullman,
2006). Competing models of interest were examined to test
whether adding Negative Affect to the model linking Disgust
Sensitivity and OCD Symptoms would improve the fit, as
compared to a model that did not include Negative Affect. The
specificity of this relationship was tested by comparing separate
models that included either measures of Contamination-Based
OCD or Non-Contamination-Based OCD. In both cases, a
structural meditational model was examined to evaluate the
degree to which the relation between Disgust Sensitivity and
Contamination-Based OCD is accounted for by Negative Af-
fect.
The full LISREL model consists of two components, the
measurement model and the structural equation model. The
measurement model shows how the measured variables, called
indicators, are associated with the latent constructs of interest
via confirmatory factor analysis (CFA). The structural equation
model specifies and tests the proposed relationships among the
indicators and latent variables.
The PRELIS system file containing the raw data served as
the input data. The Maximum Likelihood (ML) method was
used to determine the fit of the proposed models to the data.
The first indicator for each latent variable was constrained to a
factor loading of 1 to serve as a reference variable and set the
metric (Byrne, 1998). The following criteria were used to test
the models’ fit: the root-mean-square error of approximation
(RMSEA), with values greater than .10 indicating poor fit
(MacCallum, et al., 1996; McDonald & Ho, 2002); the com-
parative fit index (CFI) with values in the mid-.90 s indicating a
good fit of the data to the model (Raykov & Marcoulides,
2000), and the adjusted goodness of fit index (AGFI), with

G. SHAMS ET AL.
values greater than .95 indicative of a good fit (Byrne, 1998).
The fit of competing models was tested with the chi-square
difference test (Δχ2) and using the Akaike Information Criterion
(AIC; Akaike, 1987). In the chi-square difference test, the
chi-square statistic and the degrees of freedom for the baseline
(parent) model were subtracted from those of the nested (i.e.,
more restricted) model. The resulting chi-square value was
evaluated for the difference of the degrees of freedom from the
two models to determine if there has been loss of fit given the
new constraints. A non-significant difference in 2 between two
competing models suggests the model has not lost its goodness
of fit with additional imposed parameters. The AIC criterion is
used in model selection only and cannot be interpreted for a
single model. When comparing two competing models, the
model with the lowest AIC is considered the preferred model
(Burnham & Anderson, 2002).
Results
Validity of the PDS-R
Descriptive statistics and Pearson correlations of all study
variables were first examined .The means, standard deviations,
and ranges for each variable are shown in Table 1. Table 1 also
presents Pearson correlation coefficients between the PDS-R
and various measures of psychopathology. Consistent with our
predictions, the PDS-R was generally highly correlated with
various measures of psychopathology, with the exception of
depression. The PDS-R was most highly correlated with meas-
ures of OCD symptoms. Table 1 also shows that the PDS-R
total score demonstrated adequate internal consistency with a
Cronbach’s alpha of .87.
Table 1.
Pearson correlations between study variables.
Study
Variables 1 2 3 4 5 6 7 8 9
1. PDS-R - .95 .86 .35 .47 .46 .14 .1.26
2. CD - .67 .32 .49 .47 .1 .06.22
3. ARD - .32 .35 .35 .17 .15.26
4. OCI-R - .64 .57 .37 .32.52
5. PI - .85 .25 .22.42
6.VOCI - .25 .22.43
7. STAI-S - .67.52
8.BDI - .52
9. PSWQ -
M 50.65 36.06 14.58 20.838.25 9.45 42.12 10.5321.45
SD 15.52 10.48 6.43 10.8 6.33 7.15 11.52 9.499.21
Range 7 - 90 6 - 63 1 - 28 0 - 560 - 40 0 - 38 20 - 790 - 84 0 - 48
Alpha .87 .8 .8 .85 .86 .84 .93 .88.79
Note: CD = Contagion Disgust, ARD = Animal-Reminder Disgust, M = Mean,
SD = standard deviation. PDS-R = Persian Disgust Scale-Revised; OCI-R =
Obsessive-Compulsive Inventory-Revised; PI = Padua Inventory; VOCI = Van-
couver Obsessional Compulsive Inventory; STAI = State-Trait Anxiety, Inven-
tory-State; BDI = Beck Depression Inventory-II; PSWQ = Penn State Worry,
Questionnaire. All correlations > .14 significant at the p < .01 level.
Test-Retest Reliability of the PDS-R
Scores on the PDS-R were also highly consistent across time.
The unbiased estimate of the Intra-class correlation coefficient
(ICC) for the total score across the two time points (approxi-
mately 1 month apart) in a subsample of participants was .85.
Confirmatory Factor Analysis
Three competing models of the factor structure of the PDS-R
were tested. The first was a unidimensional (i.e., one-factor)
model, in which all 25 items were loaded onto a single Disgust
factor. The second model tested was a three-factor model com-
prised of Core Disgust (12 items), Animal-Reminder Disgust (8
items), and Contamination Disgust (5 items) reported by Ola-
tunji et al., 2007. Finally, a two-factor model comprised of
Contagion Disgust (i.e., a combination of Core and Contamina-
tion Disgust and made up of those 17 items) and “Animal-Re-
minder Disgust” (8 items) was also fit to the data. This two-
factor model was derived from prior research suggesting that
Core and Contamination Disgust may both be mediated by a
common pathogen prevention mechanism (Olatunji et al.,
2007).
As shown in Table 2, the one-factor, two-factor, and three-
factor models of the PDS-R all provided a poor fit to the data
because the RMSEA values were equal to or exceeded .10
(MacCallum et al., 1996). Therefore, the items of the PDS-R
were examined. Because items 5 (Animal-Reminder Disgust)
and 26 (Contamination and Contagion Disgust) were replaced
in their entirety during the translation process to be culturally
consistent with Persian practices, these items were removed,
and the CFA was repeated. As shown in Table 3, the three-
factor model of the PDS-R without items 5 and 26 provided an
acceptable fit to the data with χ2 (227) = 879.08, p < .001,
RMSEA = .09 The two-factor and one-factor models also pro-
vided an acceptable fit to the data. Direct comparison revealed
that the two-factor [Δχ2 (1) = 52.07, p < .001] and three factor
[Δχ2 (3) = 59.25, p < .001] solutions fit the data significantly
better than the one-factor model. The fit of the two- and
three-factor models did not significantly differ from each other
[Δχ2 (2) = 1.01, p = .60]. However, examination of the internal
consistency of the separate factors (see Table 4) revealed that
the two-factor solution appears to provide a more parsimonious
and stable factor structure for the PDS-R. That is, the internal
consistency for the combination of core and contamination
disgust (Contagion Disgust) was higher than the internal con-
sistency of either factor alone. Thus, a two-factor structure,
with items 5 and 26 removed, was retained for structural equa-
tion model analysis.
Table 2.
Summary statistics of confirmatory factor analyses of the Persian DS-R
(N = 374).
Model 2 df RMSE
A
RMSEA
90% CI CFI AGFIAIC
3-factor1340.79272.10 .098 - .11 .93 .93 1446.79
2-factor1345.16274.10 .098 - .11 .93 .93 1447.16
1-factor1400.04275.11 .10 - .11 .92 .93 1500.04
Note: df = degrees of freedom; RMSEA = root mean square error of approxima-
tion; RMSEA 90% CI = 90% confidence interval for the RMSEA; χ2diff = nested
χ2 difference.

G. SHAMS ET AL.
530
Table 3.
Summary statistics of confirmatory factor analyses of the Persian DS-R with items 5 and 26 removed (N = 374).
Model 2 df RMSEA RMSEA 90% CICFI AGFI AIC 2diff Δdf p
3-factor 879.08 227 .089 .083 - .095 .91 .93 977.08
2-factor 880.09 229 .088 .082 - .095 .91 .93 974.09 1.01 2 .60
1-factor 932.16 230 .092 .085 - .098 .91 .93 1024.16 52.07 1 <.001
Note: df = degrees of freedom; RMSEA = root mean square error of approximation; RMSEA; 90% CI = 90% confidence interval for the RMSEA; χ2diff = nested χ2 differ-
ence.
Table 4.
Internal reliability of Persian DS-R factors and corrected item-total
correlations.
Factor Cronbach’s α Item Corrected Item-Total
Correlation
Core .730 1 .23 (Food)
3 .47
6 .16 (Animals)
8 .31
11 .41
13 .29 (Sym. Magic/Food)
15 .48
17 .47
20 .37
22 .37
25 .27 (Food)
27 .52
Contamination .560 4 .31
9 .32
18 .35
23 .41
Contagion .797 1 .25 (Food)
3 .48
4 .44
6 .18 (Animals)
8 .32
9 .35
11 .43
13 .30
15 .48
17 .50
18 .44
20 .39
22 .42
23 .52
25 .29 (Food)
27 .54
Animal Reminder .795 2 .52
7 .61
10 .39
14 .49
19 .41
21 .61
24 .67
Structural Equation Models
Measurement Model 1. The first tested specified the two
factors of the Persian DS-R (i.e., Animal-Reminder Disgust
measurement model that was and Contagion Disgust) as indi-
cators for a Disgust Sensitivity latent variable. The STAI,
BDI-II, and PSWQ total scores were selected as indicators for
the Negative Affect latent variable. Finally, the Padua Inven-
tory Contamination Fear Subscale, The VOCI Contamination
Fear Subscale, and the Washing subscale of the OCI-R were
selected as indicators for the Contamination-Based OCD latent
variable. The results indicated that the measurement model was
a good fit to the data with χ2 (17) = 66.99, RMSEA = .09,
RMSEA 90% CI = .066 - .11, CFI = .7, AGFI = .91.
Measurement Model 2: The second measurement model that
was tested specified that the two factors of the Persian DS-R
(i.e., Animal Reminder Disgust and Contagion Disgust) were
indicators for the Disgust Sensitivity latent variable. The STAI,
BDI-II, and PSWQ total scores were again used as indicators
for the Negative Affect latent variable. Finally, all subscales of
OCI-R, excluding Washing, (i.e., Checking, Hoarding, Neu-
tralizing, Obsessing, and Ordering) were selected as indicators
for the Non-Contamination-Based OCD latent variable. The
results indicated that the measurement model was a good fit to
the data with χ2 (32) = 97.76, RMSEA = .076, RMSEA 90% CI
= .059 - .93, CFI = .96, AGFI = .91.
Structural Model 1: Competing models of interest were ex-
amined to test whether adding Negative Affect to the model
linking Disgust Sensitivity and Contamination-Based OCD
Symptoms would improve the fit, as compared to a model that
did not include Negative Affect. The first model (i.e., Model 1)
specified direct effects only between Disgust Sensitivity and
Contamination-Based OCD symptoms. The paths from Disgust
Sensitivity to Negative Affect and from Negative Affect to
Contamination-Based OCD symptoms were fixed to 0 in this
baseline model. Table 5 shows that the fit of this model was
poor, χ2 (19) = 108.50, RMSEA = .11. The next model tested
(i.e., Model 2) specified direct and indirect effects between
Disgust Sensitivity and Contamination-Based OCD symptoms.
All paths were freely estimated in this model. This model pro-
vided an adequate fit to the data: χ2 (17) = 65.42, RMSEA = .09,
and it was a better fit to the data than the baseline model, Δχ2 (2)
= 43.08, p < .001. Thus, adding the indirect effects of Negative
Affect to the model resulted in an improvement in fit.
The final model (i.e., Model 3) included indirect effects only
between Disgust Sensitivity and Contamination-Based OCD
symptoms. Thus, the path from Disgust Sensitivity and Con-
tamination-Based OCD symptoms was fixed to 0 in this model,
while the paths between Disgust Sensitivity and Negative Af-
fect and Negative Affect and Contamination-Based OCD
symptoms were freely estimated. This model was a poor fit to

G. SHAMS ET AL.
Table 5.
Summary statistics of structural equation models: Model 1 contamina-
tion-based OCD.
Model 2 df RMSEA RMSEA
90% CI CFI AGFIAIC
1 108.50 19 .11 .086 - .13 .95 .88 131.37
2 65.42 17 .088 .066 - .11 .97 .91 103.42
3 144.30 18 .14 .12 - .16 .93 .91 183.95
Note. Model 1: direct effects only; Model 2: direct and indirect effects;
Model 3: indirect effects only; df = degrees of freedom; RMSEA = root
mean square error of approximation; RMSEA 90% CI = 90% confidence
interval for the RMSEA; CFI = comparative fit index; AGFI = adjusted
goodness of fit index; AIC = Akaike information criterion.
the data, with χ2 (18) = 144.30, RMSEA = .14, and the model
lost its goodness of fit compared to Model 2, Δχ2 (1) = 78.88, p
< .001. All the paths estimated in Model 2 were significant, and
the standardized regression estimates are shown in Figure 1.
These results indicate that that when controlling for latent
Negative Affect, latent Disgust Sensitivity remains significant
in the relationship between Disgust Sensitivity and Contamina-
tion-Based OCD.
Structural Model 2: Competing models of interest were ex-
amined to test whether adding Negative Affect to the model
linking Disgust Sensitivity and Non-Contamination-Based
OCD Symptoms would improve the fit, as compared to a model
that did not include Negative Affect. The first model (i.e.,
Model 1) specified direct effects only between Disgust Sensi-
tivity and Non-Contamination-Based OCD symptoms. The
paths from Disgust Sensitivity to Negative Affect and from
Negative Affect to Non-Contamination Based OCD symptoms
were fixed to 0 in this baseline model. Table 6 shows that the
fit of this model was poor, χ2 (34) = 198.89, RMSEA = .11. The
next model tested (i.e., Model 2) specified direct and indirect
effects between Disgust Sensitivity and Non-Contamination
Based OCD symptoms. All paths were freely estimated in this
model. This model provided an adequate fit to the data: χ2 (32)
= 99.71, RMSEA = .076, and was a better fit to the data than
the baseline model, Δχ2 (2) = 99.18, p < .001. Thus, adding the
indirect effects of Negative Affect to the model resulted in an
improvement in fit.
The final model (i.e., Model 3) included indirect effects only
between Disgust Sensitivity and Non-Contamination-Based
OCD symptoms. Thus, the path from Disgust Sensitivity and
Non-Contamination-Based OCD symptoms was fixed to 0 in
this model, while the paths between Disgust Sensitivity and
Negative Affect and Negative Affect and Non-Contamina-
tion-Based OCD symptoms were freely estimated. This model
was an acceptable fit to the data, with χ2 (33) = 113.40,
RMSEA = .085, but the model lost its goodness of fit compared
to Model 2, Δχ2 (1) = 13.69, p < .001. All the paths estimated in
Model 2 were significant, and the standardized regression esti-
mates are shown in Figure 2. These results indicate that that
when controlling for latent Negative Affect, latent Disgust Sen-
sitivity remains significant in the relationship between Disgust
Sensitivity and Non-Contamination-Based OCD.
Discussion
The present study examined the factor structure and psycho-
metric properties of the PDS-R in a non-clinical student sample
in Iran. CFA provided initial support for two- and three-factor
models of the PDS-R. This finding is largely consistent with the
notion that disgust does not represent a unitary construct (Ola-
tunji et al., 2004). However, analysis of internal consistency
suggested that the two-factor model of contagion disgust (a
combination of core and contamination disgust with 17 items)
and animal reminder disgust (8 items) may be a more parsimo-
nious fit to the data. This finding appears to be inconsistent
with that of Olatunji and colleagues (2009), who found that the
three-factor solution of core disgust, animal-reminder disgust,
and contamination disgust provided a better fit to the data than
a one-factor model in Australia, Brazil, Germany, Italy, Japan,
Sweden, and the United States. However, Olatunji et al. (2009)
did not examine the relative fit of a two-factor model. The
two-factor model appears to be consistent with the notion that
domains of core and contamination disgust may share a com-
mon pathogen-prevention mechanism (Olatunji et al., 2007).
The PDS-R was also found to be stable over time, suggesting
that disgust sensitivity is a relatively stable construct. Although
item 5 (“I feel nauseated being in a graveyard, therefore I avoid
going there”) and item 26 (“While eating at a restaurant, you
find a stand of hair in your food”) were changed entirely in
order to establish consistency with the cultural experiences of
the participants, model fit was achieved after removal of these
items. The degree to which disgust is experienced in response
to various stimuli may differ as a function of culturally specific
variables. The content of disgust can be idiosyncratic and may
be shaped by personal experiences, as well as socio-cultural and
religious influences. According to Islamic rules (Holy Koran),
certain things are documented as being Najes (dirty, nasty im-
pure) and they are thus strictly prohibited to Muslims. Najes
include urine, feces, sperm, dogs, pigs, carrion, blood, pagans,
and wine.
In the current study, structural equation modeling revealed
that latent disgust sensitivity, defined by the contagion disgust
and animal reminder disgust subscales of the PDS-R, is sig-
nificantly associated with latent symptoms of contamination
and non-contamination based OCD. Some disgust domains
(core and contamination) might share a “common factor” (dis-
ease) that motivates specific behavioral tendencies (avoidance).
In addition, consistent with previous findings, the PDS-R was
positively correlated with contamination-based OCD symptoms
however, this association was not specific to contamination
based OCD.
Table 6.
Summary statistics of structural equation models: Model 2 non-con-
tamination-based OCD.
Model 2 df RMSEA RMSEA
90% CI CFI AGFIAIC
1 198.8934 .11 .090 - .12 .91 .86 214.00
2 99.7132 .076 .059 - .093 .96 .91 145.71
3 113.4033 .085 .069 - .10 .95 .90 164.86
Note: Model 1: direct effects only; Model 2: direct and indirect effects; Model 3:
indirect effect only; df = degrees of freedom; RMSEA = root mean square error of
approximation; RMSEA 90% CI = 90% confidence interval for the RMSEA; CFI
= comparative fit index; AGFI = adjusted goodness of fit index; AIC = Akaike
information criterion.

G. SHAMS ET AL.
532
Error
Padua Inventory
VOCI
OCI-R Washing
.73
.88
.97
Animal Reminder
Disgust
Contagion Disgust .96
.69
Negative Affect
.16 .30
.81 .80 .66
STAI Total
BDI Total
PSWQ Total
.47
Disgust
Sensitivity
Contamination-B
ased OCD
Figure 1.
Structural association between latent negative affect, disgust sensitivity, and contamination-based OCD.
.80
.45
.46
.80
.49
Error OCI-R
Ordering
OCI-R
Obsessing
OCI-R
Neutralizing
OCI-R Hoarding
OCI-R Checking
Error
Animal Reminder
Disgust
Contagion Disgust .77
.86
Negative Affect
.25 .56
.81 .79 .68
STAI Total
BDI Total
PSWQ Total
.24
Disgust
Sensitivity
N
on-C onta mi na
tion-Based
OCD
Figure 2.
Structural association between latent negative affect, disgust sensitivity, and non-contamination-based OCD.
Clinical and anecdotal evidence from Iran suggests that con-
tamination fears in patients with OCD are largely related to
feelings of spiritual impurity rather than distress about germs,
dirt or any other contamination which may cause disease or
harm. For example, Dadfar and colleagues reported that the
most common obsessive symptoms in their clinical sample was
found to be concern or disgust with bodily waste or secretions,
which according to the authors is described as a feeling of “Ne-
jasat” or “spiritual impurity” in the Iranian culture (Dadfar,
Bolhari, Malakouti, & Bayan Zadeh, 2001 ). In addition, Ghas-
semzadeh et al. (2002) reported a high frequency of obsessions
with themes of fear of impurity (62%) in their sample of 135
individuals with OCD in Iran. Therefore, while the rituals re-
volve around contamination and cleaning themes, they are tan-
gled with issues of religious contamination and purity, which
usually manifest as a fear of spiritual impurity. The role of dis-
gust in these religious-based contamination fears need to be
further investigated in the non-western and Islamic culture of
Iran. This is of particular interest as new research evidence
emerging suggests that the experience of disgust increases the
severity of moral judgment such that those high in DS tend to
make harsher moral judgments (e.g., Inbar, Pizarro, Knobe, &
Bloom, 2009, Olatunji, 2008). In this context, disgust has been
conceptualized as an evaluative sentiment that may regulate
moral behavior by identifying the objects, behaviors, or persons
which are to be avoided in order to maintain “purity” (Schnall,
Haidt et al., 2008). Therefore both the experience of disgust and
OCD symptoms may be shaped by the broader cultural factors
such as religiosity.
Overall, the present findings offer initial data on the factor
structure and psychometric properties of the PDS-R in a large
sample of Iranian college students. Although further research is
needed, it seems that the PDS-R is an excellent instrument for
the assessment of disgust phenomena and can be used in the
cultural context of Iran. However, as the participants were ho-
mogenous in age, education, and ethnicity, it is premature at
this stage to make a definite statement about the PDS-R factors
within the Iranian context. A question for future investigations
is whether the confirmatory factor structure of the present study
can be replicated with other Iranian samples, including both
clinical and non-clinical community populations. Although the
validity and reliability of the PDS-R in this study were quite
satisfactory, further studies are needed to investigate the PDS-R
in more diverse samples. There is also a need for more work on
the role of disgust in the genesis and maintenance of OCD in
different Iran and on whether it is possible to differentiate

G. SHAMS ET AL.
between different OCD sub-types based on disgust perceptions.
While there is growing interest in exploring the role of dis-
gust in psychopathology particularly in relation to OCD, there
is a big gap in the literature on cross-cultural studies in both
clinical and non-clinical samples. The current study needs to be
extended a clinical sample of OCD patients in Iran.
In addition, the overwhelming majority of research including
the current study, implicating disgust in contamination-based
OCD, is based on cross-sectional data and therefore it is diffi-
cult to draw any inferences about the possible causal directions.
Hence it would be important for future research to employ pro-
spective longitudinal design to explore whether disgust sensi-
tivity precedes the onset of OCD symptoms. Moreover, there is
new emerging evidence to suggest that cognitive processes,
specifically obsessive beliefs, may also mediate the relationship
between disgust and contamination fear (Cisler, Brady, Olatunji,
& Lohr, 2010). This is particularly relevant as new research
findings from Iran provide support for the relevance of the ob-
sessive beliefs and cognitive biases in the development and
maintenance of OCD in Iran (e.g., Ghassemzadeh, Bolhari,
Birashk, & Salavati, 2005; Mohammadi, Fata, & Yazdandoost,
2009; Shams, Karamghadiri, Esmaili Torkanbori, Rahiminejad,
& Ebrahimkhani, 2006).
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