Integration of Impulsivity and Positive Mood to Predict Risky Behavior:
Development and Validation of a Measure of Positive Urgency
Melissa A. Cyders, Gregory T. Smith, and
Nichea S. Spillane
University of Kentucky
University of Chicago Medical Center
Agnes M. Annus
University of Kentucky
University of Dayton
In 3 studies, the authors developed and began to validate a measure of the propensity to act rashly in
response to positive affective states (positive urgency). In Study 1, they developed a content-valid
14-item scale, showed that the measure was unidimensional, and showed that positive urgency was
distinct from impulsivity-like constructs identified in 2 models of impulsive behavior. In Study 2, they
showed that positive urgency explained variance in risky behavior not explained by measures of other
impulsivity-like constructs, differentially explained positive mood-based risky behavior, differentiated
individuals at risk for problem gambling from those not at risk, and interacted with drinking motives and
expectancies as predicted to explain problem drinking behavior. In Study 3, they confirmed the
hypothesis that positive urgency differentiated alcoholics from both eating-disordered and control
Keywords: impulsivity, positive mood, risky behavior, urgency, validation
The aim of this series of studies was to test the possibility that
there is an impulsivity-like construct that involves the tendency to
act rashly or maladaptively in response to positive mood states
(positive urgency) and that individual differences on this trait help
explain risky behavior. This possibility stems from the following
concerns. First, there is evidence that the term impulsivity actually
subsumes several moderately related constructs that play different
roles in accounting for risky behavior. However, none of the
existing constructs specifically reflects the capacity for risk taking
in response to positive moods. Second, there is suggestive evi-
dence that risky and maladaptive behaviors can follow very pos-
The Many Impulsivity-Like Constructs
Impulsivity is an important construct for understanding many
forms of dysfunctional behavior. In addition to being included as a
diagnostic criterion for many disorders in the Diagnostic and Statis-
tical Manual of Mental Disorders (4th ed.; DSM–IV; American Psy-
chiatric Association, 1994), impulsivity has been implicated in risk
models for a number of disorders, including alcoholism, eating dis-
orders, and pathological gambling. However, researchers have de-
diverse as acting without thinking, sensation seeking, risk taking,
boredom susceptibility, adventuresomeness, and anxious impulsivity
have all been included in discussions of impulsive behavior (Depue &
Collins, 1999; Reed & Derryberry, 2005). Conceptually, these con-
cepts appear quite diverse. There are likely to be important distinc-
tions among them. For example, there are likely many individuals
(e.g., pilots) who seek thrilling sensations but who plan carefully
before acting. Two prominent models have identified distinct sub-
scales that measure different aspects of impulsivity: the behavioral
activation system framework (Carver & White, 1994; Gray, 1987;
Patterson & Newman, 1993) and Whiteside and Lynam’s (2001)
description of the four types of impulsivity within the five-factor
model of personality.
Gray (1987) held that there are two neurological systems that
regulate certain aspects of behavior: the behavioral inhibition
system and the behavioral activation system. The behavioral inhi-
bition system is thought to be activated primarily by signals of
punishment and nonreward. Its activation results in interruption of
ongoing behavior, enhanced analysis of stimuli in the environ-
ment, and cautious responding. The behavioral activation system is
thought to be activated by signals of reward and nonpunishment.
Its activation results, primarily, in approach or reward-seeking
behavior. In mixed incentive conditions (i.e., in which both reward
and punishment cues are present), individuals high in behavioral
activation continue to act, apparently in the pursuit of reward, even
though they are being punished. That propensity earns them the
label disinhibited or impulsive (Patterson & Newman, 1993).
Whiteside and Lynam (2001) factor analyzed self-report mea-
sures of impulsivity and identified four distinct but related con-
Melissa A. Cyders, Gregory T. Smith, Nichea S. Spillane, and Agnes A.
Annus, Department of Psychology, University of Kentucky; Sarah Fischer,
Department of Psychiatry, University of Chicago Medical Center; Claire
Peterson, Department of Psychology, University of Dayton.
Correspondence concerning this article should be addressed to Melissa
A. Cyders, Department of Psychology, University of Kentucky, Lexington,
KY 40506-0044. E-mail: firstname.lastname@example.org
2007, Vol. 19, No. 1, 107–118
Copyright 2007 by the American Psychological Association
structs, each of which corresponds to a facet of one of the five
factors of personality, as measured by the NEO Personality Inven-
tory—Revised (NEO-PI–R; Costa & McCrae, 1995). The four
were sensation seeking (tendency to seek out novel and thrilling
experiences: the excitement-seeking facet of extraversion), lack of
deliberation (tendency to act without thinking: the deliberation
facet of conscientiousness), lack of persistence (inability to remain
focused on a task: the self-discipline facet of conscientiousness),
and urgency (tendency to act rashly in response to distress: the
impulsivity facet of neuroticism). There is evidence that these four
factors represent distinct constructs. In addition to representing
facets on different factors in the NEO-PI–R measure of the five-
factor model, they have different correlates and explain different
aspects of risky behavior (Fischer, Smith, & Anderson, 2003;
Miller, Flory, Lynam, & Leukefeld, 2003; Smith, Fischer, Cyders,
Annus, Spillane, & McCarthy, in press; Whiteside, Lynam, Miller,
& Reynolds, 2005).
Neither of these concepts of impulsivity includes rash action in
response to a positive mood. If there are individual differences in
the tendency to respond rashly or impulsively to extremely posi-
tive mood states, they do not appear to be captured by existing
Positive Mood–Based Rash Action: Positive Urgency
There is empirical evidence of tendencies toward rash action in
response to very positive mood. In general, induced positive mood
produces increased risk taking (Yuen & Lee, 2003). Undergradu-
ate college students are more likely to drink on days of celebration
than during the week (Del Boca, Darkes, Greenbaum, & Goldman,
2004; Kornefel, 2002), and that drinking tends to be heavy and
associated with increased physical violence, alcohol-related inju-
ries and deaths, driving while under the influence, and unwanted
sexual intercourse (Del Boca et al., 2004). There is also evidence
that some individuals engage in risky drinking to enhance an
existing positive mood. M. L. Cooper, Agocha, and Sheldon
(2000) found that drinking for mood enhancement leads to in-
creased drinking, drinking-related problems, and involvement in
risky behaviors. In addition, positive mood has been identified as
a temptation to resume gambling among recently quit pathological
gamblers (Holub, Hodgins, & Peden, 2005).
In light of this evidence, it is important to determine whether
there are individual differences in the tendency to respond to
positive mood with risky behavior. We hypothesized that rash
action in response to positive mood states is related to rash action
in response to negative mood states, and both represent an under-
lying dysregulation in response to extreme mood states.
The first step in examining this hypothesis was to develop a
measure of positive urgency. We developed a series of items to
fully tap the construct of positive urgency, subjected them to
content validity analysis by trained raters, examined their psycho-
metric properties, and determined their factor structure in a large,
developmental sample. We then tested positive urgency’s relations
with risky behaviors, its incremental validity in explaining risky
behavior over other forms of impulsivity, and its ability to differ-
entiate among disordered groups.
Study 1: Item Development, Item Refinement, and Factor
In Study 1, after developing an item pool and subjecting it to
content validity analyses, we conducted factor analyses to (a)
examine the factor structure of positive urgency and (b) determine
whether positive urgency was distinct from each type of impul-
sivity described in the behavioral activation system and five-factor
model work reported previously. The original positive urgency
items either were developed on an a priori, theoretical basis or
were adapted from items from a scale that measured negative
urgency (the tendency to act rashly when in a negative mood; this
scale is described below). This process resulted in 17 items.
Participants for the content validity portion of the study were
three trained raters. The raters were doctoral students trained
extensively in risky behavior and impulsivity through course work,
clinical experiences, and research in these areas.
Participants for the factor analytic portion of the study com-
prised two samples. The first sample included 1,322 undergraduate
students (mean age ? 19.34 years, range ? 18–40 years). Sixty-
four percent of the sample was female, and 36% was male. Ninety
percent of the sample was Caucasian, 5% African American, and
5% other. The second sample consisted of 300 college student
participants (175 women); the mean age of this second sample was
19.16 years (age range ? 18–52 years). Mean scores on scales
used in Study 1 can be found in Table 1.
Positive Urgency Measure (PUM).
tion of the PUM was the object of this investigation. The nature of
the scale and the results of its evaluation are described below.
Items are assessed on a 4-point scale ranging from 1 (agree
strongly) to 4 (disagree strongly).
UPPS Impulsive Behavior Scale—Revised (UPPS–R).
UPPS–R (Whiteside & Lynam, 2001) is a 4-point Likert-type scale
used to assess four different types of impulsivity (internal consis-
tencies in parentheses): urgency (.87), deliberation (.91), persis-
tence (.82), and sensation seeking (.90). Items are assessed on a
scale ranging from 1 (agree strongly) to 4 (disagree strongly).
Drinking Motives Questionnaire (DMQ).
DMQ (M. L. Cooper, 1994) is a 20-item scale that reflects four
main motivations for drinking, including coping motives (drinking
to cope or deal with negative affect), enhancement motives (drink-
ing to enhance positive motives), social motives (drinking to
increase socialization), and conformity motives (drinking to fit in
with a group). Items on this questionnaire are rated on a 1 (almost
never/never) to 5 (almost always/always) Likert-type scale. Each
factor has an internal consistency of .84–.85, and each item loads
uniquely on one of the four factors (M. L. Cooper, 1994).
Behavioral Activation Scale (BAS).
White, 1994) measures individual differences in behavioral acti-
vation. It is made up of 13 items divided into three subscales:
Reward Responsiveness (5 items; e.g., “When I get something I
want, I feel excited and energized”), Drive (4 items; e.g., “When
The psychometric evalua-
The BAS (Carver &
CYDERS ET AL.
I want something, I usually go all out to get it”), and Fun Seeking
(4 items; e.g., “I crave excitement and new sensations”). In the
developmental sample and the current sample, the BAS had an
overall internal consistency of .87. Items were assessed on a
4-point Likert-type scale ranging from 1 (agree strongly) to 4
Raters were trained for this study through explanation of the
new proposed trait of positive urgency. They were then asked to
differentiate items that represent positive urgency from items rep-
resenting the three BAS subscales, Whiteside and Lynam’s (2001)
four impulsivity-like constructs, and motives to drink alcohol. If
raters could consistently differentiate positive urgency items from
BAS and UPPS–R items, we would conclude that the positive
urgency items were specific in two senses: They would not be
indices of broad, general impulsivity that overlapped with other
scales, nor would they be inadvertent measures of other particular
types of impulsivity. If raters could differentiate the items from
motives to enhance positive mood by drinking, they would be
specific in the sense that they did not refer to that specific mood-
enhancement strategy. Raters were also asked to judge whether the
items clearly reflected the defined construct of positive urgency
and to identify items that did not.
Participants in the first factor analysis sample completed the
PUM in a group-administration format. Participants in the second
sample completed the PUM, the BAS, and the UPPS–R in a
group-administration format. All participants completed informed
Content Validity Analyses
Positive urgency items that were misclassified by any of the
three experts were deleted. That procedure resulted in the deletion
of 3 items. There was 100% agreement that the remaining 14 items
reflected positive urgency, and no rater mislabeled an item from
any other scale as a positive urgency item. The full set of items
resulting from this study is listed in Table 2.
Exploratory Factor Analysis of the PUM
We first conducted an exploratory factor analysis on the PUM
items, using a random sample of n ? 666 from the total sample of
1,322 participants. Because items were scored on a 4-point Likert-
type scale, we used polychoric correlations. Although we antici-
pated a one-factor solution following the content validity analysis,
we considered solutions from one to four factors. A principal
factor analysis with oblique oblimin rotation produced one factor
with a scree plot that gave clear indication of a one-factor solution.
Using parallel analysis, we found that the eigenvalue for the
second factor was smaller than the average eigenvalue produced
from 50 factor analyses of random data and smaller than the
eigenvalue at the 95th percentile of eigenvalues produced from
random data. The Factor 1 loadings were quite high, ranging from
.76 to .99. In addition, each of the highest loading items on Factor
2 loaded much more highly on Factor 1. We, therefore, concluded
that a one-factor solution best fit the data for the exploratory
subsample. The factor loadings for the PUM are presented in Table 2.
Confirmatory Factor Analysis of the PUM
We next subjected the one-factor solution to confirmatory factor
analysis on the second random sample (n ? 656), again using
polychoric correlations. We used the weighted least squares esti-
mation method. The model resulted in a comparative fit index
(Bentler, 1990) of .99 and a Tucker–Lewis fit index of .99 (Tucker
& Lewis, 1973). These values indicate an acceptable fit: Conven-
tion holds acceptable fit at .90 (Kline, 2005) or even more strin-
gently at .95 (Hu & Bentler, 1999). The model had a root-mean-
square error of approximation (Browne & Cudeck, 1993) of .06
(90% confidence interval from .05 to .07), which is considered a
fair fit. The loadings were again quite high, ranging from .59 to
.85. See Table 3 for the correlation matrix of items included in the
confirmatory factor analysis. The PUM scale, consisting of the 14
Mean Scores and Standard Deviations for Study Samples 1 and 2
Study 1Study 2
(n ? 1,322)
(n ? 300)
(n ? 326)
(n ? 216)
MSDM SDM SDM SD
impulsivity) to 4 (high impulsivity).
All scales were coded so that higher scores indicate more impulsive action. Scales ranged from 1 (low
items, showed an internal consistency of ? ? .94 in the combined
sample (n ? 1,322), with a median interitem correlation of r ? .79
(ranging from r ? .37 to r ? .85).
PUM and BAS Factor Analysis
We next conducted an exploratory factor analysis using a prin-
cipal factor analysis with an oblique rotation on the items from the
PUM and the BAS, again using polychoric correlations. Using the
rotated solution, we found that four factors had eigenvalues greater
than 1, and a scree plot strongly suggested a four-factor solution.
Using parallel analysis, we found that the four factors had eigen-
values greater than both the average eigenvalue from 50 factor
analyses of random data and the 95th percentile eigenvalue from
those 50 random data factor analyses. The first factor comprised
items from the PUM; the second factor was made up of the 5 items
on the Reward Responsiveness subscale of the BAS; the third
factor consisted of the 4 items on the Drive subscale of the BAS;
the fourth factor consisted of the 4 items on the Fun Seeking
subscale of the BAS.
There was a clear simple structure to the results of this factor
analysis. All PUM items loaded most highly on the PUM factor;
their factor loadings ranged from .82 to .91, with an average factor
loading of .86. The loading of each PUM item on the PUM factor
was at least .67 higher than its loading on any other factor. For
reward responsiveness, item loadings ranged from .78 to .97; for
drive, item loadings ranged from .75 to .94; for fun seeking, item
loadings ranged from .81 to .91. No BAS items had secondary
loadings higher than .15 on the PUM factor. The positive urgency
factor’s correlations with the other three factors were reward
responsiveness, r ? .03; drive, r ? .04; and fun seeking, r ? .36.
PUM and UPPS–R Factor Analysis
We conducted an exploratory principal factor analysis with an
oblique rotation using the PUM and UPPS–R scales, again using
Exploratory Factor Loadings for the Positive Urgency Measure Items
1. When I am very happy, I can’t seem to stop myself from doing things that can have
2. When I am in great mood, I tend to get into situations that could cause me problems.
3. When I am very happy, I tend to do things that may cause problems in my life.
4. I tend to lose control when I am in a great mood.
5. When I am really ecstatic, I tend to get out of control.
6. Others would say I make bad choices when I am extremely happy about something.
7. Others are shocked or worried about the things I do when I am feeling very excited.
8. When I get really happy about something, I tend to do things that can have bad
9. When overjoyed, I feel like I can’t stop myself from going overboard.
10. When I am really excited, I tend not to think of the consequences of of my actions.
11. I tend to act without thinking when I am really excited.
12. When I am really happy, I often find myself in situations that I normally wouldn’t be
13. When I am very happy, I feel like it is OK to give in to cravings or overindulge.
14. I am surprised at the things I do while in a great mood.
Data are from a principal factor analysis with oblique rotation. n ? 666.
Correlation Matrix for Confirmatory Factor Analysis
Item123456789 1011 12 1314
Items are labeled by item number in Table 2. All correlations were significant at p ? .01.
CYDERS ET AL.
polychoric correlations.1Using the rotated solution, we found that
five factors had eigenvalues greater than 1, and a scree plot
strongly suggested a five-factor solution. Using parallel analysis,
the five factors had eigenvalues greater than both the average
eigenvalue from 50 factor analyses of random data and the 95th
percentile eigenvalue from those 50 random data factor analyses.
As anticipated, the five factors consisted of the items measuring
positive urgency, sensation seeking, lack of perseverance, negative
urgency, and lack of deliberation.
For positive urgency, all PUM items loaded highest on the
positive urgency factor. PUM loadings ranged from .55 to .81,
with a mean loading of .68. PUM loadings were at least .22 higher
than their loadings on any other factor. For the UPPS–R scales, all
the items loaded most highly on their assigned scale. The negative
urgency item loadings ranged from .64 to .94, with a mean loading
of .75. Negative urgency items tended to have secondary loadings
on positive urgency but no primary loadings on the PUM factor.
The mean secondary loading of negative urgency items on the
PUM factor was .11. Sensation-seeking item loadings ranged from
.62 to .91, with a mean loading of .76. Lack of perseverance item
loadings ranged from .76 to .97, with a mean loading of .86. The
lack of deliberation item loadings ranged from .46 to .92, with a
mean loading of .62. The positive urgency factor’s correlations
with the other four factors were sensation seeking, r ? .21; lack of
perseverance, r ? .22; negative urgency, r ? .37; and lack of
deliberation, r ? .28 (p ? .01 in each case).
The PUM scale appears to be content valid and unidimensional.
It represents a distinct factor from those represented by the sub-
scales of the BAS and from those represented by the four scales of
the UPPS–R. Thus, positive urgency does not appear to be repre-
sented in a psychometric representation of the behavioral activa-
tion system, nor does it appear to be represented among the four
impulsivity-like facets of the NEO-PI–R. However, the current
study did not address external correlates of positive urgency; this
was the aim of Study 2.
Study 2: External Correlates of Positive Urgency
The primary aim of Study 2 was to begin to test whether positive
urgency plays a different role in explaining risky behaviors from
that of other forms of impulsivity. By risky behaviors, we mean
ill-considered actions that increase the risk of harm to the individ-
ual (e.g., trespassing, having sex with someone involved with
someone else). We thus tested (a) the hypothesis that positive
urgency would explain variance in risky behavior participation not
explained by other forms of impulsivity, and (b) the hypothesis
that positive urgency would uniquely explain variance in risky
behaviors likely to stem from an existing positive mood and, thus,
would play a different role from other forms of impulsivity.
In this study, we examined positive urgency’s relation to risky
behaviors in college students for these reasons. When adolescents
leave home, the rates of at least some forms of risky behavior tend
to increase beyond their already high adolescent levels (Budde &
Testa, 2005; Kelley, Schochet, & Landry, 2004). Late adolescent
rates of risky behavior appear not to differ as a function of college
attendance (rather, what matters is leaving adult supervision;
Budde & Testa, 2005). Thus, college student samples may reason-
ably reflect the rates of risky behavior characterizing late adoles-
cents in general. Because the rates of risky behaviors are quite high
among college students, risk-related phenomena can be studied
and are of clinical interest in this population (Hingson, Heeren,
Winter, & Wechsler, 2005). In addition, college students’ risky
behavior appears often to be associated with celebrations and good
moods: It tends to occur on weekends, college breaks, and times
without heavy school demands (Del Boca et al., 2004).
the 1,322 students who participated in Study 1. The 326 partici-
pants ranged from 18 to 52 years of age, with a mean age of 19.1
years. Fifty-two percent of the sample was male. African Ameri-
cans made up 4% of the sample, Caucasians 90%, Asian Ameri-
cans 2%, and other ethnicities 4%. All participants completed adult
written consent to participate in the study. Mean scores can be
found in Table 1.
Sample 2 participants were 216 college students;
79% of the sample were women. Ethnicities were as follows:
Caucasian 89%, African American 8%, Asian American 2%, and
other ethnicities 1%. Participants had a mean age of 18.2 years
(range ? 18–31 years). See Table 1 for mean scores.
Sample 1 participants were a subset (n ? 326) of
2 participants completed the PUM and had an internal consistency
of .94 in Sample 1 and .95 in Sample 2.
The UPPS–R was described above; it was com-
pleted by both samples and had an overall internal consistency of
.75 in Sample 1 and .89 in Sample 2.
Negative Outcome Scale (NEGO).
quency of participating in risk-taking activities that are likely to
have a negative outcome. The scale proved successful in identify-
ing risky activities, and it related to multiple forms of impulsivity
as hypothesized (Fischer & Smith, 2004). It consists of 10 items
measuring risky or impulsive behaviors. The items ask the indi-
vidual to indicate how many times in the past year they have
participated in a range of activities, with answers ranging from 1
(0 times in the past year) to 5 (16 or more times in the past year).
Sample items include stealing something valued less than $100,
trespassing, and having sex with someone who was involved with
someone else. In the current sample, the scale had an internal
consistency of ? ? .70, with a mean endorsement level of 1.59.
Sample 1 participants completed this measure.
South Oaks Gambling Screen (SOGS).
Blume, 1988) is a 20-item inventory designed to screen for Diag-
The PUM was described above. Sample 1 and Sample
This scale assesses the fre-
The SOGS (Lesieur &
1We replicated each factor analysis using Pearson product–moment
correlations. In each case, the pattern of results was the same, and all
conclusions drawn were identical. The only difference between the two
methods was that the factor loadings estimated using Pearson product–
moment correlations were consistently slightly lower than those estimated
using polychoric correlations.
nostic and Statistical Manual of Medical Disorders (3rd ed.;
DSM–III; American Psychiatric Association, 1980) criteria for
problem gambling. A score of 2 or greater is used to screen
individuals as potential problem gamblers. Several studies have
compared the classification accuracy of the SOGS to a DSM–IV-
based structured interview of problem gambling and have found it
to be highly correlated in community and treatment samples (r ?
.80; Cox, Enns, & Michaud, 2004; Stinchfield, 2002; Strong,
Lesieur, Breen, Stinchfield, & Lejuez, 2004). Sample 1 partici-
pants completed this measure.
The A.E. Max (Goldman & Darkes, 2004) is a
24-item self-report measure that assesses one’s beliefs about the
effects of alcohol consumption. The A.E. Max has eight intercor-
related first-order dimensions: Alcohol makes one social, horny,
attractive, egotistical, dangerous, sick, sleepy, and woozy. These
first-order dimensions can be thought to represent one of three
main content areas: (a) positive arousing effects, (b) negative
arousing effects, and (c) both positive and negative sedating effects
(Goldman & Darkes, 2004). Test takers rate the frequency with
which they expected that alcohol would result in each effect on a
7-point Likert-type scale ranging from 0 (never) to 6 (always). The
scale has been shown to significantly predict alcohol use (R2?
.29, p ? .001) and alcohol involvement (R2? .35, p ? .001) after
1 year in a college-age sample. Both samples completed this
measure; Cronbach’s alpha was .87 in both samples.
Drinking Styles Questionnaire (DSQ).
Carthy, & Goldman, 1995) gathers information about an individ-
ual’s alcohol use and provides two subscales. The Drinking/
Drunkenness subscale includes quantity and frequency of
consumption, proportion of time drinking leads to drunkenness,
maximum quantity consumed, and physical effects. Cronbach’s
alpha for the developmental sample was reported as .94, and scores
correlated .62 with collateral reports (Smith et al., 1995). The
Alcohol-Related Problems subscale includes problems related to
arrests, vandalism, and fights with friends and family. Cronbach’s
alpha in that sample was .84, and scores correlated .40 with
collateral reports (Smith et al., 1995). For the current study sample,
the mean scores on the Drinking Symptoms and Drinking Prob-
lems subscales were 19.5 (SD ? 7.6) and 5.6 (SD ? 1.1), respec-
tively. Both samples completed this measure.
The DMQ (M. L. Cooper, 1994) was described above.
Sample 1 participants completed this measure; the scale had a
Cronbach’s alpha of .95 in the current sample.
The DSQ (Smith, Mc-
Participants completed questionnaires in a group-administration
format. All participants were adults who provided informed con-
Positive Urgency Explains Unique Variance in Risky
Using data from Sample 1, we conducted a series of hierarchical
multiple regression analyses, with the four UPPS–R impulsivity
facets and positive urgency as independent variables and the
NEGO scale as the dependent variable. Positive urgency was
related to the NEGO, which measures risk-taking behaviors likely
to have negative outcomes (r ? .32, p ? .001; positive urgency
thus explained 10% of the variance in the risk-taking measure). We
then tested the incremental validity of positive urgency over all
four impulsivity-related constructs measured by the UPPS–R. We
entered those four measures in the first step and positive urgency
in the second step. Positive urgency significantly explained 2%
additional variance in risk taking beyond what was explained by
the four UPPS–R scales, as measured by the increment in R2. We
then examined the incremental validity of each form of impulsivity
over the others: In each of four analyses, one form of impulsivity
was entered in the second step after the other four (including
positive urgency) were entered in the first step. Other than positive
urgency, only two forms of impulsivity added significant predic-
tion to that provided by the other four: sensation seeking (8%
additional variance) and negative urgency (1%).
To test whether positive urgency explained different variance in
risky behavior, we divided the NEGO into two subscales: behav-
iors likely to occur while in a positive mood (e.g., vandalism,
sexual intercourse with someone involved with someone else) and
behaviors not likely to occur while in a positive mood (e.g.,
walking home alone late at night, hitchhiking). Positive urgency’s
correlation with positive mood behaviors (r ? .35, p ? .001) was
significantly higher than its correlation with the other behaviors
(r ? .13, p ? .05): significant difference test, t(1, 324) ? 2.81, p ?
.01. No such difference was found for any of the other four forms
of impulsivity: None explained positive mood-based risky behav-
ior better than other risky behavior. None of these correlations
differed significantly as a function of gender.
Positive Urgency Differentiates Potential Problem
Gamblers From Nongamblers
Using data from Sample 1, we identified 39 participants with
SOGS scores of 2 or greater, which indicate risk of problem
gambler status. We matched those participants with 41 nongam-
bling participants; in this subsample of 80, positive urgency cor-
related significantly with problem gambling (r ? .52, p ? .05). We
then tested the incremental validity of each scale over the other
four, using hierarchical multiple regression analyses; the same
three scales were again the only significant incremental R2predic-
tors: sensation seeking (7%), positive urgency (5%), and negative
Positive Urgency Correlates With Problem Drinking
Using data from Sample 1, we found that positive urgency
correlated significantly with drinking symptoms (quantity and
frequency of consumption, frequency of drunkenness; r ? .24, p ?
.001) and problem drinking (e.g., engaging in vandalism while
drinking, being arrested for drinking-related offenses, getting into
fights with family, etc.; r ? .27, p ? .001). Those relationships
were replicated using data from Sample 2 (with drinking symp-
toms, r ? .32, p ? .001, and drinking problems, r ? .43, p ?
.001). The relationships did not differ by gender.
We conducted hierarchical multiple regressions with both drink-
ing symptoms and drinking problems as two different dependent
variables. When each of the five scales was entered in a separate
step after the other four, only sensation seeking (4%) and negative
CYDERS ET AL.
urgency (4%) added significant incremental concurrent predictive
power for symptoms and problems, respectively, as measured by
the increment in R2.
Moderated relationships and positive urgency’s unique role in
explaining problem drinking.
Positive urgency did not have in-
cremental validity over the set of other forms of impulsivity in
explaining problem drinking in general. To see whether it plays a
unique role in understanding problem drinking, we tested a series
of interactions between the impulsivity traits and drinking motives
and drinking expectancies.
First, using data from Sample 2, we tested whether positive
urgency and the other forms of impulsivity interacted with the
motive to drink to enhance an existing positive mood. We hypoth-
esized that positive urgency would relate to problem drinking for
those who drink to enhance a positive mood but not for those who
do not drink for that reason. We further anticipated that no other
form of impulsivity would interact with the mood-enhancement
drinking motive. We conducted five hierarchical multiple regres-
sions on centered variables, each time entering the interaction term
in the second step, after entering main effects in the first step. Our
hypothesis was supported: Positive urgency alone significantly
interacted with enhancement motives (p ? .05); the interaction
significantly explained an additional 2.1% of the variance beyond
that explained by the main effects, as measured by the increment
in R2. We then probed the interaction according to guidelines from
Cohen, Cohen, Aiken, and West (2003). Positive urgency was
related to problem drinking among individuals high in the motive
to drink to enhance positive mood (? ? .43, p ? .001), but it was
not among those low in that drinking motive (? ? .16, ns). None
of the other impulsivity traits interacted with this motive to predict
Second, again using data from Sample 2, we tested hypotheses
concerning interactions of the impulsivity variables and positive,
arousing expectancies (i.e., drinking makes one more attractive,
horny, and social). We anticipated that both negative urgency, with
its emphasis on rash action to improve one’s mood by alleviating
distress, and positive urgency, with its emphasis on rash action
while experiencing a positive mood, would interact with positive,
arousing expectancies in the prediction of problem drinking. We
did not expect any other form of impulsivity to interact with this
expectancy to explain problem drinking.
Our hypothesis was again supported. Positive urgency and the
expectancy interacted significantly (p ? .001; 5.1% additional R2
variance explained). Positive urgency was significantly related to
drinking problems at high levels of the expectancy (? ? .49, p ?
.001), but it was unrelated to drinking problems at low levels of the
expectancy (? ? .05, ns). Only for those who expected drinking to
produce positive, arousing effects was positive urgency related to
drinking problems. Negative urgency and the expectancy also
interacted significantly (p ? .01; 4.1% additional explained vari-
ance). A similar pattern existed as with positive urgency: Negative
urgency was significantly related to drinking problems at high
levels of expectancy endorsement (? ? .50, p ? .001) but not at
low levels of expectancy endorsement (? ? .11, ns). None of the
other types of impulsivity interacted significantly with positive,
Third, again using data from Sample 2, we tested hypotheses
concerning interactions of the impulsivity variables and negative,
arousing expectancies (i.e., drinking makes one more egotistical or
dangerous). We anticipated that only positive urgency would in-
teract with this expectancy. We believe positive urgency leads to
a kind of celebratory loss of control that can result in behaviors
that involve risk or danger. In contrast, we do not believe negative
urgency operates this way: Negative urgency-based alleviation of
distress and mood improvement are unlikely to be associated with
dangerous or risky celebration.
This hypothesis was also supported: Negative, arousing alcohol
expectancies had a significant interaction only with positive ur-
gency (p ? .01). Negative, arousing expectancies did not predict
drinking problems for individuals low in positive urgency (? ?
–.04, ns), but they did predict drinking problems for those high in
positive urgency (? ? .28, p ? .001). Neither negative urgency
nor any other form of impulsivity interacted with negative, arous-
ing expectancies. In fact, negative alcohol expectancies were
shown to be overall unrelated to problem drinking (r ? .08, ns):
They relate to drinking only among high-positive-urgency individ-
The results of Study 2 supported our hypotheses concerning the
relationships between positive urgency and different forms of risky
behavior. Positive urgency correlated with general measures of
risky behavior, problem gambling behavior, and problem drinking
behavior. It had incremental validity over other measures in the
cases of general risky behavior and problem gambling behavior.
Most important, there was evidence that positive urgency plays a
different role from that of other forms of impulsivity in explaining
First, positive urgency was the only type of impulsivity for
which prediction of positive mood-based risky behavior was sig-
nificantly greater than prediction of other risky behavior. Positive
urgency can also differentiate individuals at risk for problem
gambling from those not at risk. Second, positive urgency was the
only type of impulsivity to significantly interact with enhancement
drinking motives to predict drinking problems. The trait of positive
urgency explained considerable variance in problem drinking for
those individuals who drink to enhance a positive mood. In con-
trast, positive urgency explained no problem drinking variance for
individuals who lack that motive.
Third, the two forms of mood-based impulsivity (positive and
negative urgency) significantly interacted with expectancies that
alcohol would make one social, attractive, or horny to predict
alcohol problems. In both cases, the traits related to problem
drinking only for individuals who expected drinking to produce
those effects. Again in contrast, for none of the other three forms
of impulsivity did this contingent relationship exist.
Fourth, positive urgency was differentiated from negative ur-
gency by examining the two variables’ role in relation to expect-
ancies that drinking would make one more egotistical or danger-
ous. These expectancies related to problem drinking for high-
positive-urgency individuals, but they did not for low-positive-
urgency individuals, nor did they for either high- or low-negative-
urgency individuals. These expectancies do not relate to problem
drinking in the sample as a whole; it appears that the only indi-
viduals for whom those expectancies relate to excessive, problem
drinking are those high in positive urgency: those inclined toward
rash action when in a very positive mood. It is likely that these
positive mood-based actions are more likely to lead to these
negative outcomes because of the external and dangerous nature of
the behaviors involved.
Thus, the first tests of positive urgency’s relations to external
criteria supported the validity and unique predictive role of the
construct. The findings, which were obtained in college students
who tend to engage in high levels of risky behavior, are of interest
to clinicians seeking to understand the high levels of risk faced by
late adolescents who have left home.
There are limitations to Study 2 and clear directions for further
research. First, the results were all cross-sectional correlations.
Although such tests are a necessary first step, there is a need for
longitudinal research on positive urgency’s role. Second, Study 2
did not address the possible role of positive urgency in psycho-
logical disorders. Study 3 begins that process.
Study 3: Differentiation of Disordered Samples
In Study 3, we conducted a rigorous test of positive urgency’s
relationship to disordered status: We hypothesized that positive
urgency would differentiate one impulsivity-related disorder from
a different impulsivity-related disorder. Excessive drinking may
well occur when one is in a positive mood. Because positive
urgency items refer to the tendency to engage in harmful or
maladaptive acts while in a positive mood, positive urgency is
likely to characterize alcoholics more than others. In contrast, we
do not believe the impulsive behavior characteristic of eating-
disordered individuals is likely to occur in response to a very
positive mood. Previous mood-based explanations of eating-
disordered behavior emphasize negative, but not positive, mood
states as precursors to binge eating (see Hohlstein, Smith, & Atlas,
1998; Leon, Fulkerson, Perry, Keel, & Klump, 1999; Podar, Han-
nus, & Allik, 1999). We, therefore, did not expect positive urgency
to characterize individuals with eating disorders.
Participants comprised three groups. All were women who
consented to participate in the study. A sample of participants with
a history of alcohol abuse was drawn from a community alcohol
abuse clinic. The sample consisted of 45 women, ranging in age
from 19 to 56 years, with a mean age of 35.6 years. All women
were diagnosed with alcohol abuse or alcohol dependence by the
clinic and were rediagnosed as part of this study. Sample charac-
teristics are described below. The eating-disordered sample was
identified through screening of 846 college women, 90 of whom
reported binge eating behaviors, purging behaviors, or both. Of
those, 37 agreed to participate, completed the study, and were
diagnosed with eating disorders via interview. Results of their
interview-based diagnoses are presented below. Not surprisingly,
the eating-disordered sample was younger, with a mean age of
19.8 years of age (range ? 18–30 years). A control sample of 35
women was drawn from the same college student pool. These
women endorsed no eating-disordered or problem drinking symp-
toms during the screening, and they were interviewed to rule out
the presence of either disorder. Ages in this sample ranged from 18
to 25 years, with a mean age of 19.3 years.
internal consistency of ? ? .95 in both the eating-disordered
sample and the alcoholic sample.
The DSQ was described above.
Eating Disorder Examination (EDE).
Cooper, 1993) is a 62-item semistructured interview designed to
assess the full range of behavioral and cognitive or attitudinal
features of the specific psychopathology of eating disorders during
the preceding 4 weeks, including patients’ extreme concerns about
their shape and weight. The EDE has been shown to have good
internal consistency (Z. Cooper, Cooper, & Fairburn, 1989), dis-
criminative validity (Z. Cooper et al., 1989), and test–retest reli-
ability (Rizyi, Peterson, Crow, & Agras, 2000).
Structured Clinical Interview I for DSM–IV (SCID–I).
SCID–I (First, Spitzer, Gibbon, & Williams, 1997) is a semistruc-
tured clinical interview to assess the presence of DSM–IV criteria
for Axis I disorders. The alcohol abuse and dependence scales for
the SCID–I were used to assess the presence of alcohol use
disorders in the sample.
The PUM was described above. The PUM had an
The EDE (Fairburn &
Participants met individually with experimenters, all of whom
were trained to conduct the required clinical interviews. They
completed the EDE interview, a question about prior eating dis-
order diagnosis, and the necessary portions of the SCID–I. To
assess eating disorders, we audiotaped the interviews and calcu-
lated interrater reliability of the EDE scales and diagnoses. For the
assessment of alcoholism, we considered agreement between the
interview and the clinic diagnosis as evidence of interrater reli-
ability. Participants then completed all questionnaire measures and
Description of Samples
All members of the alcoholic sample were clinic patients, and
100% were diagnosed with alcohol abuse or alcohol dependence
by both clinic staff and study interviewers. The mean levels of
drinking, prior to treatment onset, were drinking 16.8 days per
month, with 58.0 overall drinks consumed per month (where a
drink equals one shot of hard liquor, one glass of wine, or one
bottle of beer).
Eating disorder diagnoses were made using the EDE. Interrater
reliabilities were as follows: overall diagnosis, 1.0; frequency of
objective binge eating, 1.0; frequency of each type of purging
behavior (e.g., self-induced vomiting, diuretic use), 1.0; EDE
Restraint, .90; EDE Eating Concern, .98; EDE Weight Concern,
.94; EDE Shape Concern, .92; prior eating disorder diagnosis, 1.0.
The diagnosed sample included 28 women diagnosed with eating
disorder not otherwise specified, 2 women diagnosed with bulimia
nervosa, 2 with binge eating disorder, and 4 diagnosed with
restricting or subthreshold anorexia nervosa. The women reported
a mean objective binge episode rate of 4.9 episodes per week. The
overall eating-disordered sample had the following means on the
EDE (standard deviations in parentheses): Restraint, 3.3 (.21);
Weight Concern, 2.9 (.25); Eating Concern, 1.5 (.25); and Shape
CYDERS ET AL.
Concern, 3.2 (.27). Corresponding normative values for bulimia
nervosa patient samples have the following ranges: Restraint,
3.1–3.4; Weight Concern, 3.1–4.0; Eating Concern, 2.4–3.5; and
Shape Concern, 3.5–4.1 (Fairburn & Cooper, 1993). Thus, al-
though these women were not identified at a clinic setting, they
reported similar levels of dysfunction to identified clinic patients.
Differentiation of Disordered Groups by Positive Urgency
We conducted a one-way analysis of variance (ANOVA) with
planned contrasts to test the hypothesis that alcoholic patients
would have higher levels of positive urgency than both eating-
disordered and control participants, who would not differ from
each other (see Table 4). The omnibus one-way ANOVA indicated
that level of positive urgency differed according to group, F(2,
114) ? 4.70, p ? .01. The first planned contrast (all contrasts were
one-tailed tests of a priori hypotheses) compared the alcoholic
group to the average of the eating-disordered and control groups.
As hypothesized, alcoholics had higher levels of positive urgency
than the average of the other two groups, t(1, 114) ? 2.80, p ? .01.
Next, we compared the eating-disordered group to the control
group. Again as expected, those two groups did not differ on
positive urgency, t(1, 114) ? 1.27, ns. The third contrast compared
the alcoholic group to the eating-disordered group. As hypothe-
sized, the alcoholic group had higher levels of positive urgency
than the eating-disordered group, t(1, 114) ? 1.84, p ? .05. Thus,
alcoholic women endorsed higher levels of positive urgency than
did eating-disordered women or control women.
Women with eating disorders tend to have high rates of sub-
stance use (Bulik, Sullivan, Carter, & Joyce, 1997). It is, therefore,
possible that, even though the eating-disordered sample was sig-
nificantly lower than the alcoholic sample on positive urgency, the
magnitude of the difference was attenuated because of the pres-
ence of heavy-drinking women in the eating-disordered sample. To
investigate this possibility, we defined consuming more than 20
drinks in the previous month as heavy drinking. Of the 37 women
in the eating-disordered sample, 18 reported consuming more than
20 drinks in the past month, and 19 reported consuming 20 or
fewer drinks in the past month. We conducted the same ANOVA
tests, this time comparing the alcoholic sample, the control sample,
and the sample of 19 less heavy-drinking eating-disordered
women. The same pattern of statistical significance emerged, ex-
cept that the difference between the alcoholic and eating-
disordered groups was larger. The mean positive urgency score for
the eating-disordered group was almost identical to that of the
control group (see Table 4). The two eating-disordered subsamples
did not differ on any eating-disorder symptom measure.
The results of Study 3 indicate that positive urgency may have
importance in understanding psychological disorders beyond col-
lege student risk taking. Because alcohol consumption is likely to
be associated with positive moods and eating-disordered behavior
is not, we proposed that positive urgency would differentiate
between alcoholism and eating disorders. This hypothesis was
supported. Eating-disordered women did not differ from control
women on positive urgency, but both groups had lower mean
scores than did alcoholic women. It may, therefore, be true that
positive urgency plays a different role from other impulsivity-like
constructs. Recent studies have identified negative urgency as a
correlate of both problem drinking symptoms and eating disorder
symptoms (Smith et al., in press). Thus, perhaps both problem
drinking behavior and eating-disordered behavior can reflect a
tendency to act rashly when distressed, but only problem drinking
behavior is likely to reflect a tendency to act rashly when in a very
One limitation of Study 3 is that all members of the alcoholic
sample were currently in treatment, but the eating-disordered sam-
ple was recruited from a college student population. It is possible
that the eating-disordered women were less distressed, and their
apparently lower levels of positive urgency were an artifact of
distress level. We consider this possibility unlikely for several
reasons. First, it is certainly possible that the opposite would occur:
Maladaptive, positive urgency scores might be higher among those
not in treatment than among those in treatment. Second, the rates
of specific eating disorder diagnoses in this sample are comparable
to those of typical clinic populations, in which the majority of
patients have a diagnosis of eating disorder not otherwise specified
(Fairburn & Bohn, 2005; Martin, Williamson, & Thaw, 2000;
Turner & Bryant–Waugh, 2004). Third, even though the majority
of eating-disordered young adults have not been diagnosed, and
disordered college students are often unaware of the need for
treatment (Hoek, 2001; Perkins, Klump, Iacono, & McGue, 2005),
over half of this sample (51.2%) had been previously diagnosed
Mean Scores and Analysis of Variance (ANOVA) of Positive Urgency Based on Disordered
ControlEating disordered Alcohol abuse
nM SDnM SDnM SD
heavy-drinking individuals from the eating-disordered sample. Groups with common subscripts were not
different in planned contrast tests.
**p ? .01.
First ANOVA was performed on whole sample. Second ANOVA was performed after removing
with an eating disorder by either mental health or medical profes-
Our theoretical perspective on the importance of describing and
measuring positive urgency was based on both psychometric/
validity and substantive, clinical considerations. Recent validity
theory has contended that theoretical clarity and more successful
prediction follow the use of precise, specific traits rather than
broad, cumulative traits (Edwards, 2001; Schneider, Hough, &
Dunnette, 1996; Smith, Fischer, & Fister, 2003). One empirical
demonstration of this perspective is that measurement of the spe-
cific traits of negative urgency, sensation seeking, lack of plan-
ning, and lack of persistence has been useful. Different traits
explain different types of risky, maladaptive behavior; this finding
is not available to users of broad impulsivity scales (Fischer &
Smith, 2004; Smith et al., in press; Whiteside & Lynam, 2003).
From a substantive, clinical perspective, there is reason to think
that individual differences in the tendency to engage in rash action
while in a very positive mood are important and are not measured
by existing impulsivity-like scales (Del Boca et al., 2004; Yuen &
Lee, 2003). In three studies, we provided evidence that this trait
can be measured in a content-valid, reliable way, and we con-
firmed that it is distinct from all specific impulsivity-like con-
structs identified by either the behavioral activation system ap-
proach (Gray, 1987) or the five-factor model of personality
approach (Whiteside & Lynam, 2001). It explains unique variance
in risky behavior among college students, a population character-
ized by high levels of risky behaviors. It does so in theoretically
consistent ways: It explains positive mood–based risky behavior
far better than do other forms of impulsivity, and it explains
nonpositive mood risky behavior worse than do other forms of
impulsivity. It differentiates individuals at risk for problem gam-
bling from those not at risk. It explains problem drinking behavior
only conjointly with certain drinking motives and expectancies, as
predicted by theory. For individuals who drink to enhance a
positive mood or those who expect drinking to make them more
social, attractive, horny, egotistical, or dangerous, positive urgency
predicts problem drinking. For individuals low on those motives or
expectancies, positive urgency is unrelated to problem drinking.
No other form of impulsivity has the same conjoint predictive
relationships with motives and expectancies. As hypothesized, it
differentiates between the kind of impulsivity characterized by
alcoholics and the kind characterized by eating-disordered indi-
viduals. These initial findings are very encouraging, concerning
the ultimate validity, theoretical importance and clinical relevance
of the positive urgency construct.
Several lines of research will provide further tests of the validity
of the construct and, perhaps, further evidence for its clinical
utility. Longitudinal studies in which positive urgency predicts the
subsequent onset of, or increases in, various risky behaviors will
be important. Laboratory mood-induction studies may prove use-
ful: High-positive-urgency individuals should demonstrate greater
risk taking after induction of positive mood than should low-
positive-urgency individuals. Attempts to delineate which types of
risky behaviors are likely to follow positive moods can also be
To the degree that continued validity evidence accrues for the
positive urgency construct, clinical researchers may better be able
to identify important underpinnings for different clinical syn-
dromes. Impulsivity plays a prominent role in the diagnosis and
presumed etiology of many psychiatric disorders. Borderline per-
sonality disorder, antisocial personality disorder, attention-deficit/
hyperactivity disorder, mania, bulimia nervosa, dementia, sub-
stance abuse, gambling, as well as the whole section on impulse
control disorders (such as intermittent explosive disorder), all have
impulsivity as a diagnostic criterion on the DSM–IV. As it has
become clear that the term impulsivity means different things in
different contexts, the need to clarify the specific nature of rash or
impulsive action for different disorders has become apparent. For
which disorders does impulsivity follow an unusually positive
mood? For which does it not? Clinicians in practice may also
benefit from consideration of the construct of positive urgency.
Rash action following a very positive mood may require different
interventions from rash action to alleviate distress or rash action
due to an inability to plan ahead.
The current series of studies had limitations. First, all the data
presented are cross-sectional in nature. Second, the clinical find-
ings were restricted to women; we do not know whether the same
effects are present in men. We studied clinical samples of women
only because we wanted to compare alcoholism and eating disor-
ders without gender confounds. Certainly, the finding that positive
urgency worked in comparable ways for men and women in the
college student samples is encouraging. Third, the data were
gathered by self-report; we as yet know nothing about behavioral
observations or peer ratings of positive urgency. Fourth, as noted
above, although the Study 3 eating-disordered sample was reliably
diagnosed as significantly disturbed, we do not know whether an
in-treatment, clinical sample of eating-disordered women would
report different levels of positive urgency.
There is a great deal more to do to understand where positive
urgency fits into the matrix of psychological constructs. Because
positive urgency related most highly to negative urgency in this
study, it may be the case that the two constructs represent different
aspects of emotional dyscontrol. If so, perhaps they should be tied
to theories of mood regulation. Relatedly, it is important to under-
stand how positive urgency relates to broader, integrative theories
of psychopathology. For example, broad models of externalizing
behavior may ultimately need to integrate fine-grained analyses of
personality risk factors such as positive and negative urgency.
Because there do appear to be some individuals for whom risk
taking occurs in response to positive mood states, this integrative
work will be important to do.
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Received August 25, 2005
Revision received September 14, 2006
Accepted September 19, 2006 ?
CYDERS ET AL.