Exercise 4 Cardiovascular FitnessThis exercise describes thre.docx

Exercise 4: Cardiovascular Fitness
This exercise describes three assessments designed to help you
estimate the present level of your cardiovascular fitness. The
term “cardiovascular” refers to both the heart and blood vessels.
Since the health of the respiratory system is so closely linked to
that of the blood circulation, fitness of both will be referred to
as “cardiorespiratory”.
During your fitness assessment, stop any test immediately if
you begin to feel any pain, faintness, or dizziness. If you notice
any other disturbing sensations such as headache or inability to
get enough air, do not complete the test.
DETERMINING TARGET HEART RATE ZONE
If your heart rate becomes faster during physical activity, that
exercise would be described as aerobic. These activities, when
performed often enough and at long enough durations, result in
increased efficiency of the circulatory and respiratory systems.
The noted improvements are described as the training effect.
The training effect has occurred if the heart rate is lower for a
given type of exercise or if it takes more resistance to achieve
the same heart rate.
In order to experience the training effect and improved
cardiorespiratory health, the exercise duration should exceed
twenty continuous minutes and the heart rate should be within
the target heart rate zone.
There are numerous formulae that have been developed to
determine an individual’s target heart rate. The range should

ensure sufficient stress to result in improvement, yet not exceed
what would be safe. The formula that follows is based on age
and resting heart rate.
Resting heart rate is a simple way to assess current level of
cardiorespiratory fitness. The average resting heart rate for
healthy adults is 75, and ranges from 60-100 BPM (beats per
minute). Well-conditioned endurance athletes have resting rates
in the 30s and individuals with poor fitness would exhibit
resting rates that are much higher. Powerful hearts and lungs
circulate more oxygen per beat and can maintain the body at
rest with fewer beats per minute.
True resting heart rate is a value that stays constant for days,
weeks, and longer, unless cardiovascular changes take place.
When measuring HR for these exercises, make sure to record it
when you are truly at rest. This will be the lowest value that
you record.
Use the calculations in Figure 5.1 as a guide to determine your
personal target heart rate zone. Your heart rate should be
within these limits whenever you participate in aerobic
activities. Calculations should be revisited from time to time as
resting heart rate will change with advancing age and, more
frequently, due to effects of lifestyle changes.
MinimumMaximum
Maximum heart rate in healthy adult: 220 220
Subtract your age -20-20

200 200
Subtract resting heart rate - 60- 60
140 140
Multiply by
Lower and upper limit % .70.85
98
119
Add back resting heart rate +60 +60
Target heart rate zone (BPM) 158
179
Figure 4.1. Target heart rate zone calculations for a 20 year old
with a RHR of 60 BPM.
You will be measuring your heart rate by taking your pulse with
your fingers. There are
multiple locations on the surface of the body that can be used as
pulse sites. The most commonly used arteries are the carotid
and radial.
Pulse sites, also known as pressure points, are depicted in
Figure 5.2. Pulse is recorded by gently pressing on the arterial
wall with fingers.

Figure 4.2. Pulse sites.
ASSESSMENT #1 - EXERCISE RECOVERY RATE
1. Sit quietly for 10 minutes and measure your resting heart
rate.
2. Jog in place or perform another aerobic exercise until your
heart rate reaches the minimum target heart rate 5 beats.
3. Continue to exercise at the same stress level for 3 minutes.
Don’t allow your heart rate to drop below the lower target heart
rate or exceed the maximum target heart rate 5 beats.
4. Measure your heart rate during the last 15 seconds of
exercising.
5. Measure your heart rate at 1-minute intervals until the rate is
below 100 bpm. This is your recovery rate. If your recovery
rate exceeds 5 minutes, your cardiovascular fitness is
considered below average.
ASSESSMENT #2 - HARVARD STEP TEST
1. Step on a step first with one foot and then with the other foot
until you are standing with the knees unbent. Then step down
with one foot followed by the other foot to return to the starting
position.
2. Step at a pace that results in approximately 30 times per
minute for a 5-minute period.

3. Sit down and rest. Measure your heart rate 1 minute after
exercising. Remain sitting and measure your heart rate at the 2-
minute and 3-minute intervals after exercising.
4. Calculate the sum of the 3 heart rate values. Use this formula
to calculate a fitness index:
Fitness Index = 30,000
Sum of the Three
Heart Rates
Fitness Index
Rating
<55
Poor
55 – 64
Low average
65 – 69
Average
70 – 79
High average
80 – 89
Good
90 and above
Excellent
Figure 4.3. Harvard step test fitness index.
ASSESSMENT #3 - THREE-MINUTE SIT-AND-STAND TEST
1. Sit quietly for 10 minutes and measure your resting heart
rate.
2. Starting from a sitting position with arms folded across your
chest, stand up and sit down at a rapid but comfortable pace.

Do this for three minutes. Your legs must be straight before
you sit down and you must sit down completely. Do not use a
chair with wheels.
3. Measure your heart rate during the last 15 seconds of
exercising
4. Sit down and rest. Measure your heart rate at 30 seconds, 1
minute, and 2 minutes after you sit down.
5. Circle your resting, exercise, and 3 post-exercise heart rates
in Table 5.1. Record the corresponding values written in bold in
the first column of the table.
Resting
Exercise
30 Sec
1 Min
2 Min
20
44
80
64
56
56
19
48
84
68
60
60
18
52

88
72
64
64
17
56
92
76
68
68
16
60
96
80
72
72
15
62
100
84
76
76
14
64
104
88
80
80
13
66
108
92
84
84
12
68

112
96
88
88
11
70
116
100
92
92
10
72
120
104
96
96
9
74
124
108
100
100
8
76
128
112
104
104
7
78
132
116
108
108
6
80

136
120
112
112
5
84
140
124
116
116
4
88
144
128
120
120
3
92
148
132
124
124
2
96
152
136
128
128
1
100
156
140
132
132
Table 4.1. Three-minute sit-and-stand fitness rating.

6. Calculate the sum of the values for the 5 heart rates. If the
sum is 0 – 35, your cardiovascular fitness is considered to be
dangerously poor. Your cardiovascular fitness is average when
the sum is 36 – 70. When the sum exceeds 70, your
cardiovascular fitness is excellent.
Lab Report
Report the results of all three assessments. These can be done
on yourself or any adult subject who volunteers. However, all
three assessments should be done for the same individual.
Your report should include (1) calculations for the subject’s
target heart rate zone, (2) recorded heart rates as indicated in all
charts and tables, (3) verbal (in sentence form) fitness
descriptions for all three assessments, and (4) a general
discussion of the results.
Are the results consistent among the tests? Can you draw any
conclusions based on relationships between fitness and other
lifestyle parameters? Is the subject a smoker, overweight, or an
active, fit person?
A minimum of two photographs is required. These should
depict the general appearance of your subject and the subject
engaged in at least one of the fitness assessments. Your HCC
Hawk Card or some other form of photo ID needs to be within
every image. The ID should be on or near the subject when you
take the picture.

The chair used for the sit-and-stand test needs to be sturdy and
not upholstered, on casters (wheels) or a folding chair.
Assessments should be completed either on different days or
with a minimum of an hour between tests. This is necessary to
remove fatigue as a possible explanation for diminished
performance.
6
Assessment
2014, Vol. 21(1) 82 –91
© The Author(s) 2012
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1073191112436665
asm.sagepub.com
Traditional social science theories frequently offer single-
variable explanations for complex behaviors. This is par-
ticularly true of efforts to explain crime. Crime has been
ascribed to social disadvantage, family structure, peer rela-
tions, opportunity, and a host of other factors but only occa-
sionally (e.g., Thornberry, 1987) do we see serious attempts
to integrate these concepts. For research, policy, and clini-
cal reasons, the desire on the part of many in the field to
have an integrated perspective is strong. In fact, three of the
most currently popular theories of crime, Moffitt’s (1993)
dual trajectory model, Gottfredson and Hirschi’s (1990)
self-control model, and Sampson and Laub’s (1993) age-
graded life-course model of informal social control, have

sought to integrate traditional sociological concepts with
principles from biology, psychology, and economics,
respectively. Despite these promising developments, the
field of criminology remains a field dominated by single-
variable theories. As such, it has a long way to go before it
can offer a broad-based integrated perspective on crime. It
is my contention that greater progress could be made toward
developing a broad-based integrated perspective on crime if
scholars working in the fields of criminology and criminal
justice would examine moderating and mediating effects
between variables from different models.
When discussing moderator and mediator variables the
first order of business is properly defining the terms because
they are often confused with each other even though they
represent two distinct processes. As outlined in a classic
paper by Baron and Kenny (1986), a moderator affects the
direction or strength of the relationship between an inde-
pendent or predictor variable and a dependent or outcome
variable, whereas a mediator accounts, in part or in whole,
for the relationship between an independent or predictor
variable and dependent or outcome variable. To say that
race moderates the criminal thinking–recidivism relation-
ship means that the criminal thinking–recidivism rela-
tionship is weaker or reversed for some races than others
(e.g., Caucasian vs. African American). To say that educa-
tion mediates the race–recidivism relationship means that
education accounts, at least in part, for the race–recidivism
436665ASM21110.1177/10
73191112436665WaltersAssessment
© The Author(s) 2012
Reprints and permissions:
sagepub.com/journalsPermissions.nav

1Federal Correctional Institution–Schuylkill, P.O. Box 700,
Minersville,
PA 17954, USA
2Kutztown University, Kutztown, PA, USA
Corresponding Author:
Glenn D. Walters, Department of Criminal Justice, Kutztown
University,
Kutztown, PA 19530-0730, USA.
Email: [email protected]
Relationships among Race, Education,
Criminal Thinking, and Recidivism:
Moderator and Mediator Effects
Glenn D. Walters1,2
Abstract
Moderator and mediator relationships linking variables from
three different theoretical traditions—race (subcultural
theory), education (life-course theory), and criminal thinking
(social learning theory)—and recidivism were examined
in 1,101 released male federal prison inmates. Preliminary
regression analyses indicated that racial status (White, Black,
Hispanic) moderated the relationship between criminal thinking,
as measured by the General Criminal Thinking (GCT)
score of the Psychological Inventory of Criminal Thinking
Styles (PICTS), and recidivism. Further analysis, however,
revealed
that it was not racial status, per se, that moderated the
relationship between the PICTS and recidivism, but educational
attainment. Whereas the PICTS was largely effective in
predicting recidivism in inmates with 12 or more years of
education,
it was largely ineffective in predicting recidivism in inmates
with fewer than 12 years of education. When education and

the GCT score were compared as possible mediators of the
race–recidivism relationship only the GCT successfully
mediated this relationship. Sensitivity testing showed that the
GCT mediating effect was moderately robust to violations
of the sequential ignorability assumption on which causal
mediation analysis rests. Moderator and mediator analyses are
potentially important avenues through which theoretical
constructs can be integrated and assessment strategies devised.
Keywords
race, education, criminal thinking, recidivism, moderation,
mediation
Article
http://crossmark.crossref.org/dialog/?doi=10.1177%2F10731911
12436665&domain=pdf&date_stamp=2012-02-11
Walters 83
relationship through a causal chain of events that runs from
race to education to recidivism. The purpose of the current
study was to examine moderator and mediator relationships
between concepts from three different theories of crime—
race (subcultural theory), education (life-course theory),
and criminal thinking (social learning theory)—and recidi-
vism as a means of demonstrating how these constructs
might be integrated for both theoretical and research
purposes.
Whether crime is assessed with official arrest records or
offender self-report, African American men are more often
arrested, convicted, and incarcerated for crime, and more
often report criminal involvement than their European

American counterparts (Federal Bureau of Investigation,
2010; Sampson & Wilson, 1995). Rates of recidivism tend
to be higher and survival times lower in African American
men when African American men and European American
men are compared (Jung, Spjeldnes, & Yamatani, 2010;
Sabol, Minton, & Harrison, 2007). Black–White differ-
ences in crime are particularly pronounced when analyses
are restricted to more serious offenses (Piquero & Brame,
2008). Research further indicates that Hispanics commit
fewer crimes than Blacks but significantly more crimes
than Whites (Steffensmeier, Feldmeyer, Harris, & Ulmer,
2011). In all likelihood, the race–crime relationship is
driven by a combination of cultural, structural, and social
psychological factors, but it remains a central feature of
several criminological theories, to include culture conflict
theory (Sellin, 1938), social disorganization theory (Burgess
& Bogue, 1967), and, most particularly, Wolfgang and
Ferracuti’s (1967) subculture of violence perspective.
Education has also been found to correlate with recidi-
vism and is considered a criminogenic need capable of sig-
nificantly reducing an adult or juvenile offender’s future
chances of offending (Vieira, Skilling, & Peterson-Badali,
2009). In their age-graded life-course theory of offending,
Sampson and Laub (1993) discuss the role of marriage,
military service, employment, and education in transition-
ing youthful offenders away from crime during late adoles-
cence and early adulthood. According to Sampson and
Laub (1993), these life-course changes have the power to
divert a youthful offender from a criminal trajectory to a
noncriminal trajectory. Hence, educational achievement
should be capable of reducing future recidivism. Results
from a series of recent studies, in fact, suggest that educa-
tional attainment during incarceration is associated with a
significantly lower rate of recidivism as well as a significant
reduction in the seriousness of subsequent offenses

(Blomberg, Bales, Mann, Piquero, & Berk, 2011). School
attendance was particularly beneficial to African American
males but was effective in preventing future criminality
across all racial and gender groups (Blomberg, Bales, &
Piquero, 2012).
In contrast to the sociological bent of most criminologi-
cal theories, the social learning model takes a decidedly
psychological approach to crime. With an emphasis on
individual psychology rather than group process, social
learning theory considers crime a function of observa-
tional learning, covert reinforcement, and cognitive medi-
ation. As part of his social learning–influenced criminal
lifestyle model, Walters (2012) proposes the existence of
six cognitive mediators (criminal thinking styles, attribu-
tions, outcome expectancies, efficacy expectancies, goals,
and values). These cognitive variables are held to mediate
important relationships between crime-relevant variables.
Using the General Criminal Thinking (GCT) score from
the Psychological Inventory of Criminal Thinking Styles
(PICTS: Walters, 1995) as a measure of criminal thinking,
Walters (2009, 2011b, 2011c) has shown that the PICTS is
capable of predicting recidivism after age and criminal
history had been controlled. It is important for both theo-
retical and practical reasons to understand if status vari-
ables such as race and gender moderate this relationship
and whether the GCT mediates important relationships
between social status variables and recidivism.
The first goal of this study was to evaluate whether race
moderates the relationship between criminal thinking and
recidivism. In other words, is the criminal thinking–recidi-
vism relationship invariant across race? Although research
has failed to identify consistent PICTS score differences
between Black, White, and Hispanic inmates (Walters,
2002), there is some indication that race may moderate the

relationship between the PICTS and release outcome
because recidivism is most effectively predicted in
White inmates and least effectively predicted in Hispanic
inmates (Walters, 1997; Walters, Frederick, & Schlauch,
2007). As a self-report measure, the PICTS requires a sixth-
grade reading level or higher to complete. Education—
which tends to be lower in Black and Hispanic inmates than
in White inmates (Harlow, 2003)—and not race, may there-
fore be responsible for moderating the relationship between
the PICTS and recidivism. The second goal of this study
was to evaluate whether education and the PICTS mediate
the relationship between race and recidivism. Using causal
mediation analysis and sensitivity testing (Imai, Keel, &
Tingley, 2010), the mediating effect of both education and
the GCT score on the race-recidivism relationship were
evaluated.
Method
Participants
The current sample was composed of 1,101 male inmates
released from prison less than 42 months after providing
a valid PICTS at a medium security federal correctional
84 Assessment 21(1)
institution. At the time of their release, participants ranged in
age from 19 to 78 years (M = 35.71, SD = 9.81) and had
between 3 and 20 years of education (M = 11.33, SD = 1.94).
More than two thirds of the sample was Black (68.1%), with
the remainder of the sample consisting of White (19.6%),
Hispanic (11.5%), and Asian/Native American (0.8%) par-
ticipants. Nearly three quarters of the sample listed their
marital status as single (73.3%), whereas 19.5%, 6.6%, and

0.5% listed their marital status as married, divorced, and
widowed, respectively. The two most common instant
offenses in the current sample were drugs (27.5%) and
parole/supervised release violations (27.1%); the remain-
der of the sample was serving time for weapons violations
(16.3%), robbery (9.3%), violent crimes (5.3%), property
crimes (4.3%), and miscellaneous offenses (10.2%).
Psychological Inventory
of Criminal Thinking Styles
The PICTS (Walters, 1995) is an 80-item self-report mea-
sure designed to assess the eight thinking styles believed to
support a criminal lifestyle. Each PICTS item is scored on
a 4-point Likert-type scale (strongly agree, agree, uncer-
tain, disagree), with strongly agree responses being
assigned four points, agree responses three points, uncer-
tain responses two points, and disagree responses one point
on all scales except Defensiveness-revised (Df-r) where
items are reverse-scored (strongly agree = 1, agree = 2,
uncertain = 3, disagree = 4). The PICTS yields two 8-item
validity scales—Confusion-revised (Cf-r) and Df-r—eight
8-item nonoverlapping thinking style scales—Mollification
(Mo), Cutoff (Co), Entitlement (En), Power Orientation
(Po), Sentimentality (Sn), Superoptimism (So), Cognitive
Indolence (Ci), and Discontinuity (Ds)—four factor
scales—Problem Avoidance (PRB), Infrequency (INF),
Self-Assertion/Deception (AST), and Denial of Harm
(DNH)—two composite scales—Proactive Criminal
Thinking (P) and Reactive Criminal Thinking (R)—and a
General Criminal Thinking (GCT) score. The GCT score,
which is the sum of the raw scores for the eight PICTS
thinking style scales, served as the criterion for criminal
thinking in the current investigation. Research indicates
that the GCT is an internally consistent (α = .95) measure
with strong test-retest reliability (r = .84-.86, after 12
weeks), and moderate predictive validity (unweighted

mean r with recidivism = .22: Walters, 2002).
Procedure
The base sample from which data used in the current study
were drawn contained 3,039 male inmates who had com-
pleted the PICTS between 2003 and 2010 as part of a rou-
tine intake screening procedure held in a medium security
federal correctional institution in the northeastern United
States. Nearly half the base sample (n = 1,435, 47.2%) had
been released from prison at some point during the follow-
up. In light of research showing that the predictive efficacy
of the PICTS drops off when the test-release interval
(number of months between completion of the PICTS and
release from prison) exceeds 41 months (Walters, 2011c),
132 inmates with test-release intervals greater than 41
months were removed from the sample. One hundred and
forty inmates who spent fewer than 12 months in the com-
munity and showed no evidence of recidivism and 62
inmates who produced invalid PICTS profiles (20 or more
unanswered items, Cf-r ≥ T-score of 95, or Df-r ≥ T-score
of 68; Walters, 2011c) were also eliminated from the study.
This resulted in a final sample of 1,101 male inmates who
recidivated during the follow-up or were recidivism-free
for at least 12 months in the community.
The independent (predictor) variable for the moderator
analyses was the PICTS GCT score. Race (White and
Asian/Native American = 1, Black = 2, Hispanic = 3) and
education (<12 years = 1, ≥12 years = 2) served as modera-
tor variables. Recidivism constituted the dependent (out-
come) variable. A review of electronic files from the Federal
Bureau of Investigation’s National Crime Information
Center and Federal Bureau of Prisons’ inmate data base
were used to construct the recidivism outcome (yes = 1, no
= 0). Technical parole violations were counted as evidence

of recidivism when they resulted in the individual’s return
to prison. Nearly two thirds of the sample (65.8%) received
at least one charge. For the purposes of comparison and
incremental validity, two additional predictor variables
were included in some of the analyses: age at time of release
from prison and the violation factor score from the Lifestyle
Criminality Screening Form (LCSF-V; Walters, White, &
Denney, 1991). The LCSF-V was employed as a proxy for
criminal history in this study.
Moderator analyses were conducted in two stages. In the
first stage, the dichotomous recidivism outcome measure
(Y) was regressed onto the GCT score (X), moderator vari-
able (Z; either race or education), and GCT-moderator
interaction (XZ) as part of a Cox survival regression analy-
sis that considers both the recidivism event (0/1) and time
until recidivism. If the interaction was significant, nonlinear
quadratic terms (X2, Z2) were added to the equation and
inserted before the interaction term and the analysis recom-
puted (Cortina, 1993; Lubinski & Humphreys, 1990). In the
second stage, receiver operating characteristic (ROC) curves
were calculated separately for White, Black, and Hispanic
inmates and for inmates with less (<12 years) and more
(≥12 years) education. ROC analyses were also calculated
for inmates with less and more education within each of the
three racial groups. Cox regression analyses were also used
to test the incremental validity of the GCT relative to age at
Walters 85
time of release and criminal history (LCSF-V). These anal-
yses were conducted separately for each educational level
(<12 years, ≥12 years) in the full sample as well as in each
of the three racial subsamples.

A mediation analysis was conducted with race (White
and Asian/Native American = 1, Black and Hispanic = 2) as
the independent variable, recidivism as the dependent (out-
come) variable, and education and GCT each considered as
mediator variables. Given the prospective nature of the
current design (i.e., race preceded education/GCT and
education/GCT preceded recidivism) a mediation analysis
was justified. Causal mediation analysis was performed with
algorithms devised by Imai and his colleagues (Imai, Keel,
& Tingley, 2010; Imai, Keel, Tingley, & Yamamoto, 2010).
The (continuous) mediator models (education, criminal
thinking) were fit with linear least squares regression and
the (binary) outcome model (recidivism) was fit with probit
regression. After completing Step 1 of the procedure, output
objects were bootstrapped 1,000 times with replacement
using a nonparametric mediational analysis. Sensitivity to
violations of the sequential ignorability assumption (absence
of confounders that could explain the mediation results)
was tested with a sensitivity analysis of the mediation and
outcome models using two covariate confounders: age (in
years) and the LCSF-V score.
Results
Descriptive statistics for and intercorrelations between the
three theoretical variables (race, education, GCT) and the
single-outcome measure (recidivism) are provided in Table 1.
As the results indicate, all three theoretical variables cor-
related significantly among themselves and each predicted
recidivism.
Moderator Analyses
Table 2 summarizes the results of the first set of moderator
analyses. Cox regression analysis of race as a moderator
variable revealed a significant interaction between the
PICTS GCT score and race (left column of Table 2). When

nonlinear quadratic terms (X2, Z2) were added to the equa-
tion, the PICTS GCT effect remained significant (Wald = 5.43,
p < .05) but the interaction became nonsignificant (Wald = 3.60,
p = .06). Replacing race with education as the moderator of
the GCT–recidivism relationship resulted in a Cox regres-
sion analysis in which none of the main effects (GCT,
education) or the interaction effect (education × GCT)
achieved statistical significance (right column of Table 2).
It should be noted, however, that before the interaction term
was introduced into the model, both the GCT (Wald = 33.19,
p < .001) and education (Wald = 4.27, p < .05) main effects
were significant. At first glance, these results seem consis-
tent with the conclusion that race exerted a moderating
effect on the GCT–recidivism relationship in this study.
Further analysis, however, revealed that the significant
interaction between race and the GCT became nonsignifi-
cant when quadratic terms were added to the estimated
model and that although education did not achieve a statis-
tically significant interaction with GCT, inclusion of the
education × GCT interaction term eliminated a highly sig-
nificant GCT effect, something the race × GCT interaction
failed to do even when quadratic terms were added to the
model.
To further investigate the ability of race and education to
moderate the GCT–recidivism relationship, ROC analyses
were computed by race (White, Black, Hispanic) and edu-
cation (<12 years, ≥12 years). As the results outlined in
Table 3 indicate, the GCT score achieved the most accurate
results with White participants, the least accurate results
Table 1. Descriptive Statistics and Correlations for the Three
Predictors and Recidivism Outcome.
Correlations

Predictor Mean SD Range Education GCT Recidivism
Race 1.80 0.40 1-2 −.15** .11** .12**
Education 11.33 1.94 3-20 −.12** −.08*
PICTS GCT 113.85 27.20 67-217 .17**
Recidivism 0.66 0.48 0-1
Note. Race = White (1) versus Black or Hispanic (2); Education
= years
of education; PICTS GCT = General Criminal Thinking score of
the
Psychological Inventory of Criminal Thinking Styles; SD =
standard
deviation; Range = high and low scores on this particular
measure in the
current sample; N = 1101.
*p < .01. **p < .001.
Table 2. Summary of Cox Regression Results With Race and
Education as Moderator Variables.
Race moderator Education moderator
Predictor Wald [95% CI] Wald
[95% CI]
PICTS GCT 21.85** 1.02 [1.012, 1.029] 0.26 1.00 [0.993,
1.011]
Moderator 8.51* 2.09 [1.275, 3.442] 2.79 0.58 [0.308, 1.099]
Moderator ×
GCT
8.73* 0.99 [0.990, 0.998] 2.38 1.00 [0.998, 1.009]

Note. Figures reported are the final logistic regression results at
the end of Block
2; Predictor = predictor variables; PICTS GCT = General
Criminal Thinking score
of the Psychological Inventory of Criminal Thinking Styles;
Moderator = moderator
variable, either race (White = 1, Black = 2, Hispanic = 3) or
education (1 = less
than 12 years, 2 = 12 or more years); Moderator × GCT =
moderator (either race
or education) by GCT interaction; Wald = Wald statistic with a
χ2 distribution
and one degree of freedom; exp(β) = exponent of the
unstandardized coefficient
in the form of an odds ratio (numbers below 1.00 indicate a
negative relationship
with the criterion and numbers above 1.00 indicate a positive
relationship with the
criterion); 95% CI = 95th percentile confidence interval for the
exponent of the
estimated coefficient; N = 1101.
*p < .01. **p < .001.
exp( )βx
s exp( )βx
s
86 Assessment 21(1)
with Hispanic participants, and intermediate results with
Black participants. A z-test procedure designed to compare
independent ROC curves, nonetheless, failed to detect any

significant differences in the area under the curve (AUC)
results between Blacks and Whites, z = 0.87, p > .10, Blacks
and Hispanics, z = 1.04, p > .10, or Hispanics and Whites,
z = 1.58, p > .10. A significant difference in accuracy did
arise, however, when higher and lower educated partici-
pants were compared, z = 2.20, p < .05. A z-test comparison
of dependent ROC curves revealed that the PICTS GCT
score was significantly more accurate in predicting recidi-
vism in higher educated participants than age at time of
release, z = 2.14, p < .05, and the LCSF-V was significantly
more accurate than the GCT in predicting recidivism in
lower educated participants, z = 2.40, p < .05.
A second round of Cox regression analyses were con-
ducted using all three indicators (age at time of release,
LCSF-V, PICTS GCT) as predictors for the purpose of
determining whether the PICTS GCT score possessed
incremental validity relative to age and the LCSF-V. When
the analyses were restricted to inmates with higher educa-
tional levels, the GCT score displayed incremental validity
relative to age at time of release and LCSF-V (left half of
Table 4). When the analyses were restricted to inmates with
lower educational levels, the GCT score failed to achieve
incremental validity relative to age at time of release and
the LCSF-V (right half of Table 4). When these analyses
were conducted on the three racial groups separately, the
GCT score achieved incremental validity relative to age and
the LCSF-V in White (Wald = 8.39, p < .01) and Black
(Wald = 14.24, p < .001) inmates with higher educational
levels but not in White (Wald = 2.20, p >.10) and Black
(Wald = 3.31, p =.07) inmates with lower educational
levels. Although the GCT score failed to demonstrate incre-
mental validity in either higher (Wald = 1.07, p > .10) or
lower (Wald = 0.55, p > .10) educated Hispanic partici-
pants, the beta value was in the predict direction (positive)

in the higher educated Hispanic group and in the nonpre-
dicted direction (negative) in the lower educated Hispanic
group.
Table 3. Receiver Operating Characteristic Results by Race and
Educational Level.
Description N % Invalid GCT Age LCSF-V
Total sample 1,101 5.3 .604 [.569, .639]** .595 [.560, .630]**;
.612 [.576, .647]**
Education ≥ 12 672 4.4 .628 [.584, .671]** .565 [.521, .609]*
.592 [.548, .636]**
Education < 12 429 6.7 .546 [.488, .605] .618 [.558, .677]**
.645 [.584, .706]**
White 224 4.8 .640 [.568, .713]** .621 [.547, .695]* .603 [.528,
.678]*
Education ≥ 12 170 4.6 .658 [.577, .740]** .579 [.492, .665]
.605 [.520, .690]*
Education < 12 54 5.7 .540 [.373, .707] .691 [.534, .848]* .591
[.425, .757]
Black 750 5.1 .602 [.557, .646]** .572 [.528, .616]* .591 [.546,
.636]**
Education ≥ 12 448 4.5 .618 [562, .674]** .553 [.498, .608]
.576 [.520, .632]*
Education < 12 302 5.9 .558 [.484, .632] .578 [.502, .655]*
.613 [.533, .693]*
Hispanic 127 7.3 .543 [.442, .644] .668 [.574, .762]** .638
[.542, .735]*
Education ≥ 12 54 1.8 .619 [.467, .771] .587 [.432, .741] .523
[.366, .679]
Education < 12 73 11.0 .499 [.365, .633] .714 [.592, .835]*
.706 [.584, .827]*
Note. Description = description of sample in terms of race
(White, Black, Hispanic) and education (12 or more years, less

than 12 years); N = sample
or subsample size; % Invalid = proportion of Psychological
Inventory of Criminal Thinking Styles (PICTS) that were
removed because they were invalid;
GCT = PICTS General Criminal Thinking score; Age = age at
time of release (inverse scored); LCSF-V = Violation factor of
the Lifestyle Criminality
Screening Form; the first number in the GCT, Age, and LCSF-V
columns is the area under the ROC curve (AUC) and the next
set of numbers [in brack-
ets] is the 95% confidence interval of the AUC.
*p < .05. **p < .001.
Table 4. Summary of Incremental Validity Cox Regression
Analyses for Inmates With ≥12 Years of Education and for
Inmates With <12 Years of Education.
Education ≥ 12 years
(n = 672)
Education < 12 years
(n = 429)
Predictor Wald
exp( )βx
s
[95% CI] Wald
exp( )βx
s
[95% CI]

Age 6.95* 0.87 [0.78, 0.96] 15.45** 0.78 [0.69, 0.88]
LCSF-V 22.69** 1.27 [1.15, 1.40] 16.85** 1.25 [1.12, 1.38]
PICTS GCT 21.70** 1.24 [1.13, 1.36] 2.93 1.10 [0.99, 1.24]
Note. Figures reported are the final logistic regression results at
the
end of Block 2; Predictor = predictor variables; Age = age at
time of
release from prison; LCSF-V = violation factor of the Lifestyle
Criminality
Screening Form; PICTS GCT = General Criminal Thinking
score of the
Psychological Inventory of Criminal Thinking Styles; Wald =
Wald statistic
with a χ2 distribution and one degree of freedom; exp(β) =
exponent
of the x-standardized (M = 0, SD = 1) coefficient in the form of
an odds
ratio (numbers below 1.00 indicate a negative relationship with
the cri-
terion and numbers above 1.00 indicate a positive relationship
with the
criterion); 95% CI = 95th percentile confidence interval for the
exponent
of the estimated coefficient.
*p < .01. **p < .001.
Walters 87
Mediator Analyses
In causal mediation analysis a significant mediating effect
is defined by a 95% confidence interval that does not
include zero (Imai, Keel, & Tingley, 2010; Imai, Keel,

Tingley, et al., 2010). As indicated by the results in Table 5,
education failed to mediate the relationship between race
and recidivism. The GCT score, on the other hand, success-
fully mediated the race-recidivism relationship (see Table 6).
Further analysis revealed that the mediating effect of the
GCT score on the race-recidivism relationship was only
partial (direct effect still significant) and that it accounted
for approximately 15% of the total variance in this relation-
ship. When the GCT score was used to mediate the education–
recidivism relationship the total model effect failed to
achieve significance (95% confidence interval = −0.0142
to 0.0045).
Sensitivity testing was performed on the mediation
and outcome models using age and the LCSF-V score as
confounding covariates. The results indicated a rho (ρ)
at which mediation equals zero of .16 (see Figure 1).
Coefficients of determination (R2) for the mediator and out-
come models were used to construct a graph of the amount
of variance that an unobserved confounder would have to
explain to totally eliminate the mediation effect of GCT on
the race–recidivism relationship (see Figure 2). According
to the graph, an unobserved confounding variable or set of
variables would need to account for approximately 14% of
the variance in the mediator and 14% of the variance in the
outcome to reduce the mediation effect to zero.
Regression analyses in which GCT was regressed onto
age and the LCSF-V score revealed an R2 of .018 (media-
tion model) whereas logistic regression analyses in which
recidivism was regressed onto age and the LCSF-V pro-
duced a pseudo-R2 of .067 (outcome model). Only the bino-
mial logistic regression results (outcome model) exceeded
the minimum requirements for explaining recidivism
(R2 ≥ .035) or GCT (R2 ≥ .04), and even then the amount of

mediator variance required to bring the mediation effect
down to zero would have had to have been more than 45%.
Instead, the two covariate confounders accounted for only
1.8% of the variance in the mediator variable.
Discussion
The results of this study suggest that testing for moderation
and mediation can be helpful in integrating concepts from
different theories. Patterns of moderation and mediation
Table 5. Results of a Causal Mediation Analysis in Which
Education Served as a Mediator of the Race–Recidivism
Relationship.
Effect type Point estimate 95% CI
Mediation effect
(Race → Educ
→ Recid)
0.0051 [−0.0043, 0.0157]
Direct effect
(Race → Recid)
0.0968 [0.0244, 0.1699]
Total effect 0.1019 [0.0299, 0.1713]
Proportion of
total effect via
mediation
0.0494 [0.0297, 0.1627]
Note. Race = White (1) versus non-White (2). Educ = education

(in
years); Recid = recidivism (yes = 1, no = 0); Point Estimate =
estimate
of the size of the effect; 95% CI = 95% confidence interval of
the point
estimate; N = 1101.
Table 6. Results of a Causal Mediation Analysis in which
General Criminal Thinking Served as a Mediator of the Race-
Recidivism Relationship.
Effect type Point estimate 95% CI
Mediation effect (Race
→ GCT → Recid)
0.0159 [0.0053, 0.0294]
Direct effect (Race →
Recid)
0.0906 [0.0173, 0.1592]
Total effect 0.1066 [0.0362, 0.1783]
Proportion of total
effect via mediation
0.1477 [0.0887, 0.4288]
Note. Race = White (1) vs. non-White (2). Educ = education (in
years);
Recid = recidivism (yes = 1, no = 0); Point estimate = estimate
of
the size of the effect; 95% CI = 95% confidence interval of the
point
estimate; N = 1,101.

-1.0 -0.5 0.0 0.5 1.0
ACME(ρ)
Sensitivity Parameter: ρ
A
ve
ra
ge
M
ed
ia
tio
n
E
ffe
ct
: δ
(t
)
0.
2
0.
2
0.

1
0.
1
0.
0
Figure 1. Sensitivity analysis of the binary recidivism outcome
and continuous criminal thinking mediator.
The dashed line represents the estimated mediation effect, the
solid line
represents the estimated average mediation effect at different
levels of ρ,
and the gray region represents the 95% confidence interval for
estimated
average mediation effect at different levels of ρ.
88 Assessment 21(1)
may provide clues as to how different concepts might fit
together in an integrated theory. A concept central to life-
course theory (educational attainment), for instance,
moderated the criminal thinking–recidivism relationship
whereas a concept central to subcultural theory (race) did
not. Likewise, a concept central to social learning theory
(criminal thinking) mediated the race–recidivism relation-
ship whereas educational attainment did not. In the modera-
tor analyses, the GCT score was able to predict recidivism
in individuals with 12 or more years of education but it
failed to predict recidivism in individuals with fewer than
12 years of education. Because 61% of the current sample
had 12 years or more of education and more than two thirds

of samples such as the present one dropped out of high
school prior to completing the 12th grade (Walters et al.,
1991), a substantial portion of participants in the current
study probably earned their GEDs in prison. Educational
attainment in prison has been found to exert a protective
effect against future recidivism (Blomberg et al., 2011);
and in the current study, it displayed a moderating effect on
the GCT–recidivism relationship but failed to mediate the
race–recidivism relationship.
If one follows the logic that interaction between an inde-
pendent variable and putative moderator variable in a
regression equation equals a moderator effect, then race
moderated the criminal thinking–recidivism relationship in
this study. However, when nonlinear monotonic terms for
the independent and moderator variables (X2 and Z2) were
included in the regression equation (Cortina, 1993; Lubinski
& Humphreys, 1990), the previously significant interaction
effect turned nonsignificant. In addition, even though the
education × GCT interaction failed to achieve statistical
significance in this study, the inclusion of this interaction
term in the regression equation completely eliminated an
effect for GCT that had been significant without the interac-
tion. Further analyses were accordingly conducted for the
purpose of disentangling the possible confounding effect of
education on race. In these analyses, AUC values were cal-
culated for subgroups divided by race (White, Black,
Hispanic) and education (<12 years, ≥ 12 years). Rice and
Harris (2005) have determined that an AUC of .556 is com-
parable to a small effect size, an AUC of .639 is comparable
to a moderate effect size, and an AUC of .714 is comparable
to a large effect size. Effect size estimates for individuals
with 12 or more years of education were of low-moderate to
moderate magnitude across all three racial conditions,
whereas effect size estimates for individuals with fewer
than 12 years education were of zero to small magnitude for

White, Black, and Hispanic respondents. These results indi-
cate that education was the moderating variable in this
study and that differences in efficacy across the three racial
groups were an artifact of a greater proportion of Black
(40.3%) and Hispanic (57.5%) than White (24.1%) inmates
with fewer than 12 years of education.
Whereas moderation provides information on the rela-
tive stability or invariance of a relationship, mediation pro-
vides information on the nature of a relationship. In the
current study, criminal thinking was found to partially
mediate the relationship between race and recidivism.
Hence, criminal thinking was differentially associated with
race and recidivism such that a portion of the relationship
between race and recidivism was explained by criminal
thinking. Other cognitive and noncognitive variables prob-
ably also mediate the race–recidivism relationship. One
recent study, in fact, confirmed that Black inmates had sig-
nificantly more positive outcome expectancies for crime
than White inmates (Walters, 2011a). Unfortunately,
because there was no outcome variable (e.g., recidivism) in
that study no conclusions about mediation can be drawn.
Even so, Walters (2012) includes outcome expectancies and
criminal thinking styles in his social learning theory of cog-
nitive mediation. A study in which all six cognitive medi-
ators (criminal thinking styles, attributions, outcome
expectancies, efficacy expectancies, goals, and values) are
considered simultaneously would provide more information
on the role of cognitive mediation in the race–recidivism
relationship.
-0.08
-0.06
-0.04

-0.02
0
0.4 0.8
0.
0
0.
2
0.
4
0.
6
0.
8
1.
0
RM
~2
ACME(RMRY), sgn](λ2λ3) = 1
~2 ~2
R
Y
~2

0.
1
0.
3
0.
5
0.
7
0.
9
0.0 0.1 0.2 0.3 0.5 0.6 0.7 0.9 1.0
Figure 2. Sensitivity analysis of the binary recidivism outcome
and continuous criminal thinking mediator.
Contour lines represent the estimated average mediational effect
at
different levels of an unobserved confounder. The “0” line
indicates how
strong the unobserved confounder must be to completely
eliminate the
mediation effect. Therefore, if the unobserved confounder
accounts for
20% of the variance in the outcome (R̃
2
y) then it must also account for at
least 11% of the variance in the mediator (R̃
2
M) to reduce the mediation

effect to zero. If the unobserved confounder accounts for 20%
of the
variance in the mediator (R̃
2
M) then it would need to account for 10% of
the variance in the outcome (R̃
2
y) to reduce the mediation effect to zero.
Walters 89
There are methodological, theoretical, practical, and
policy implications to the current findings that need to be
discussed. Methodologically, the current results indicate
that equating interaction with moderation is problematic in
several respects. First, there is the problem of Type I errors.
As reported by Lubinski and Humphreys (1990) and others
(Cortina, 1993; Ganzach, 1997), a spurious interaction
effect can surface if quadratic terms are not added to the
regression equation. In the current study, the significant
race × GCT interaction disappeared once quadratic terms
for the independent and moderator variables were added to
the model. Second, there is the problem of Type II errors.
Whereas a Type I error entails rejecting a null hypothesis
that is true, a Type II error involves failing to reject a null
hypothesis that is, in fact, false. Interaction has been known
to suffer from low power (Cronbach, 1987; Lubinski &
Humphreys, 1990). Adding quadratic terms to the estimated
model can reduce the power of the interaction term even
further (Cortina, 1993) and may be why the race × GCT
interaction went from significant (p < .01) to nonsignificant
(p = .06) after the quadratic terms had been added. Be this

as it may, dividing the sample along racial and educational
lines made it clear that the chief moderating variable in the
current study, even though it did not produce a significant
interaction effect, was education. In testing mediation of the
race–recidivism relationship I relied on a relatively new
procedure development by Imai, Keel, & Tingley (2010)
and Imai, Keel, Tingley, et al. (2010). Despite its recent
arrival on the methodological scene, Imai et al.’s procedure
is well documented and supported by mathematical proofs.
In addition, it has the capacity for sensitivity testing, some-
thing that is missing from most structured equation model-
ing programs currently used to assess mediation effects.
The current study also has implications for theory devel-
opment. Given the plethora of single variable models in
criminology the task of organizing and integrating these
models into a single comprehensive theory is truly formi-
dable. Integration could nonetheless be made easier with
the aid of moderation and mediation analysis. In the current
study, concepts central to three different criminological
models—race (subcultural theories), education (life-course
theories), and criminal thinking (social learning theories)—
were integrated using moderation and mediation method-
ologies. The results indicated that education but not race
moderated the criminal thinking–recidivism relationship
and that criminal thinking but not education mediated the
race-recidivism relationship. Moderation and mediation
analysis can be very useful in determining which constructs
from various models belong together and which ones do
not. The mediation analyses were particularly helpful in
clarifying important criminological relationships. From the
mediation results we can surmise that the effect of race on
recidivism is at least partially mediated by cognitive factors
such as criminal thinking, as well as, perhaps, by positive
outcome expectancies for crime (Walters, 2011a), lower

investment in marriage (King & South, 2011), and social-
environmental differences between Black and White
Americans (Phillips, 2002; Sampson, Morenoff, &
Raudenbush, 2005). Future research could further clarify
important theoretical relationships by examining how these
and other potential mediating variables interact to shape
and influence the race–recidivism relationship.
The current results, in addition to their methodological
and theoretical implications, have potentially important
practical implications, particularly for assessment. A self-
report inventory is only as good as its ability to be compre-
hended by those who complete it. The PICTS items were
originally written to reflect a 6th-grade reading level but
subsequent analyses have shown that some of the items
require as much as a 9th- or 10th-grade reading level
(Walters, 2002). Completing 12 years of education or receiv-
ing a GED were proxies for a sufficient level of reading
ability in the current study. Most of the inmates who partici-
pated in this study (61%) attained one of these two markers
and were effectively classified for recidivism risk at a low-
moderate to moderate level by the PICTS GCT score. The
remaining 39% of the sample, however, could not be effec-
tively classified for recidivism risk based on the results of
the PICTS GCT score. These findings lead to the following
two conclusions. First, we must rely more on demographic
and historical measures such as age and the LCSF-V and
less on self-report measures such as the PICTS when evalu-
ating individuals with limited educational attainment and
reading ability. Second, for the PICTS to be effective with
a larger segment of the inmate population, the item content
may need to be revised so that individuals with less than a
12th-grade education or 9th-grade reading level can com-
prehend the items sufficiently well enough to construct a
valid protocol. Classification of offenders on the basis of
race would be both illegal and unethical. The current results,

however, point to a factor (i.e., criminal thinking) that may
be partially responsible for racial differences in recidivism
and which could lay the groundwork for a more rationale
and effective offender classification system.
Both the moderation and mediation results from this
study have implications for criminal justice policy and cor-
rectional decision making. The policy implications of the
moderating effect of education on the efficacy of the PICTS
GCT score in predicting recidivism is that guidelines need
to be established for the proper use of the PICTS in identify-
ing criminogenic needs and assigning offenders to pro-
grams and levels of supervision. Depending on the
offender’s education or reading level, alternative proce-
dures (criminal history and other static risk factors) may
need to be employed for the purpose of making program
and classification decisions. The policy implications of the
race → criminal thinking → recidivism mediation effect is
that it emphasizes the necessity of including a criminal
90 Assessment 21(1)
thinking component in secondary and tertiary prevention
programs for minority youth. Although criminal thinking is
a criminogenic need in offenders of all races and at all ages
(Andrews, Bonta, & Wormith, 2006), its mediating role in
the race–recidivism relationship suggests that it may be a
particularly salient goal for interventions with African
American and Hispanic youth.
Limitations
A principal limitation of the moderator results of this study
is that reading ability was not tested. If reading ability rather
than education moderates the relationship between criminal

thinking, as measured by the PICTS GCT score, and recidi-
vism, then additional research is required using a validated
adult measure of reading comprehension such as the Adult
Basic Learning Examination (ABLE) or Tests of Adult Basic
Education (TABE). In addition, factors other than or in addi-
tion to reading level may moderate the GCT–recidivism
relationship. Motivation may be one such factor. Lack of
motivation could lead someone to drop out of school as well
as exert less than maximum effort on a psychological inven-
tory. The GCT–recidivism relationship could consequently
be moderated by reading ability, motivation, and other cor-
relates of education, but not race. Understanding moderation
is a little like peeling back the layers of an onion. Race rep-
resents the outer layer of the onion, education the next layer,
reading level and motivation the layer below this, and so on
until we reach the core moderator v ariable(s).
The mediation results also suffer from a significant limi-
tation. The confounding covariates in the sensitivity analy-
sis should predate the treatment (independent) variable. In
the current study, the treatment or independent variable was
race. Because race is present at birth it was not possible to
find confounding covariates in the current database that
preceded the independent variable. This is a limitation but
not a fatal flaw of the mediation analysis. The use of pre-
treatment confounders is based on the assumption that the
independent and mediator variables were assigned ran-
domly to participants. Using a nonmanipulated independent
variable such as race will always raise questions about
sequential ignorability. In the current study, the conditions
for prospective analysis were satisfied—that is, the indepen-
dent variable (race) preceded the mediating variable (GCT)
that then preceded the dependent variable (recidivism)—and
the two potential confounders (age and prior criminal his-
tory as represented by the LCSF-V), although not predating
the independent variable, did predate the mediator.

Moreover, in light of the fact that age and criminal history
are often considered the two best predictors of recidivism
(Gendreau, Little, & Goggin, 1996), potentially significant
confounders of the race → GCT → recidivism mediated
pathway were examined and found not to present a serious
challenge to the robustness of the mediation effect.
Sampling was a limitation that affected both the modera-
tion and mediation analyses. Because participants in this
study were incarcerated federal male offenders we do not
know how well these results apply to nonincarcerated, state,
or female offenders. Additional research is consequently
required to test the generalizability of the current results to
populations different from those included in the current
study. Generalizability is also a limitation of the PICTS in
the sense that it was only capable of effectively predicting
recidivism in participants with 12 or more years of educa-
tion. Lowering the reading level of some of the PICTS
items should make the test more applicable to a larger por-
tion of the inmate population, but it will never make it
accessible to the entire inmate population. There will always
be inmates who will need to be evaluated with demographic,
historical, or behavioral indicators. This is a limitation
inherent to all self-report measures of criminality, but it is a
limitation we must endure if our goal is to assess criminal
thinking.
Author’s Note
The assertions and opinions contained herein are the private
views
of the author and should not be construed as official or as
reflect-
ing the views of the Federal Bureau of Prisons or the United
States
Department of Justice.

Declaration of Conflicting Interests
The author declared the following potential conflicts of interest
with
respect to the research, authorship, and/or publication of this
article:
Glenn D. Walters is the author of the Psychological Inventory
of
Criminal Thinking Styles (PICTS) and receives remuneration
from
sales of the PICTS manual.
Funding
The author received no financial support for the research,
author-
ship, and/or publication of this article.
References
Andrews, D. A., Bonta, J., & Wormith, S. J. (2006). The recent
past and near future of risk and/or need assessment. Crime &
Delinquency, 52, 7-27.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator
variable distinction in social psychological research: Concep-
tual, strategic, and statistical considerations. Journal of Per-
sonality and Social Psychology, 51, 1173-1182.
Blomberg, T. G., Bales, W. D., Mann, K., Piquero, A. R., &
Berk, R. A. (2011). Incarceration, education and transition
from delinquency. Journal of Criminal Justice, 39, 355-365.
Blomberg, T. G., Bales, W. D., & Piquero, A. R. (2012). Is
educational

achievement a turning point for incarcerated offenders across
race
and sex? Journal of Youth and Adolescence, 41, 202-216.
Burgess, E., & Bogue, D. J. (Eds.). (1967). Urban sociology.
Chicago, IL: University of Chicago Press.
Walters 91
Cortina, J. M. (1993). Interaction, nonlinearity, and multicol-
linearity: Implications for multiple regression. Journal of
Management, 19, 915-922.
Cronbach, L. J. (1987). Statistical tests for moderator variables:
Flaws in analyses recently proposed. Psychological Bulletin,
102, 414-417.
Federal Bureau of Investigation. (2010). Crime in the United
States: 2009. Washington, DC: Author.
Ganzach, Y. (1997). Misleading interaction and curvilinear
terms.
Psychological Methods, 2, 235-247.
Gendreau, P., Little, T., & Goggin, C. (1996). A meta-analysis
of the predictors of adult offender recidivism: What works!
Criminology, 34, 575-607.
Gottfredson, M., & Hirschi, T. (1990). A general theory of
crime.
Stanford, CA: Stanford University Press.
Harlow, C. W. (2003). Education and correctional populations
[Bureau of Justice Statistics Special Report]. Washington,

DC: U.S. Department of Justice.
Imai, K., Keele, L., & Tingley, D. (2010). A general approach
to
causal mediation analysis. Psychological Methods, 15, 309-334.
Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2010).
Causal medi-
ation analysis using R. In H. D. Vinod (Ed.), Advances in social
science research using R (pp. 129-154). New York, NY:
Springer.
Jung, H., Spjeldnes, S., & Yamatani, H. (2010). Recidivism and
survival time: Racial disparity among jail ex-inmates. Social
Work Research, 34, 181-189.
King, R. D., & South, S. J. (2011). Crime, race, and the
transition
to marriage. Journal of Family Issues, 32, 99-126.
Lubinski, D., & Humphreys, L. G. (1990). Assessing spurious
“moderator effects”: Illustrated substantively with the hypoth-
esized (“synergistic”) relation between spatial and mathemati-
cal ability. Psychological Bulletin, 107, 385-393.
Moffitt, T. E. (1993). Adolescence-limited and life-course
persis-
tent antisocial behavior: A developmental taxonomy. Psycho-
logical Review, 100, 674-701.
Phillips, J. A. (2002). White, black, and Latino homicide rates:
Why the difference? Social Problems, 49, 349-374.
Piquero, A. R., & Brame, R. W. (2008). Race-crime and
ethnicity-
crime relationship in a sample of serious adolescent delin-

quents. Crime & Delinquency, 54, 390-422.
Rice, M. E., & Harris, G. T. (2005). Comparing effect sizes in
follow-up studies: ROC area, Cohen’s d, and r. Law and
Human Behavior, 29, 615-620.
Sabol, W. J., Minton, T. D., & Harrison, P. M. (2007). Prison
and
jail inmates at midyear 2006 (NCJ 217675). Bureau of Justice
Statistics Bulletin. Washington, DC: Bureau of Justice
Statistics.
Sampson, R. J., & Laub, J. H. (1993). Crime in the making:
Path-
ways and turning points through life. Cambridge, MA: Har-
vard University Press.
Sampson, R. J., Morenoff, J. D., & Raudenbush, S. (2005).
Social
anatomy of racial and ethnic disparities in violence. American
Journal of Public Health, 95, 224-232.
Sampson, R. J., & Wilson, W. J. (1995). Toward a theory of
race,
crime, and urban inequality. In J. Hagan & R. D. Peterson
(Eds.), Crime and inequality (pp. 37-54). Stanford, CA: Stan-
ford University Press.
Sellin, T. (1938). Culture and conflict in crime. New York, NY:
Social Science Research Council.
Steffensmeier, D., Feldmeyer, B., Harris, C. T., & Ulmer, J. T.
(2011). Reassessing trends in black violent crime, 1980-2008:
Sorting out the “Hispanic effect” in Uniform Crime Reports
arrests, National Crime Victimization Survey offender esti-
mates, and U.S. prisoner counts. Criminology, 49, 197-251.

Thornberry, T. P. (1987). Toward an interactional theory of
delin-
quency. Criminology, 25, 863-892.
Vieira, T. A., Skilling, T. A., & Peterson-Badali, M. (2009).
Matching court-ordered services with offender needs: Predict-
ing treatment success with young offenders. Criminal Justice
and Behavior, 36, 385-401.
Walters, G. D. (1995). The Psychological Inventory of Criminal
Thinking Styles: Part I. Reliability and preliminary validity.
Criminal Justice and Behavior, 22, 307-325.
Walters, G. D. (1997). Predicting short-term release outcome
using the LCSF and PICTS. Journal of Mental Health in Cor-
rections Consortium, 43, 18-25.
Thinking Styles (PICTS): A review and meta-analysis. Assess-
ment, 9, 278-291.
Thinking Styles and Psychopathy Checklist: Screening Ver-
sion as incrementally valid predictors of recidivism. Law and
Human Behavior, 33, 497-505.
Walters, G. D. (2011a). Black-white differences in positive out-
come expectancies for crime: A study of male federal prison
inmates. Journal of Criminal Justice, 39, 192-197.
Walters, G. D. (2011b). Predicting recidivism with the
Psycholog-
ical Inventory of Criminal Thinking Styles and Level of Ser-
vice Inventory–Revised: Screening Version. Law and Human
Behavior, 35, 211-220.

Walters, G. D. (2011c). Taking the next step: Combining incre-
mentally valid indicators to improve recidivism prediction.
Assessment, 18, 227-233.
Walters, G. D. (2012). Crime in a psychological context: From
career
criminals to criminal careers. Thousand Oaks, CA: SAGE.
Walters, G. D., Frederick, A. A., & Schlauch, C. (2007). Post-
dicting arrests for proactive and reactive aggression with the
PICTS proactive and reactive scales. Journal of Interpersonal
Violence, 22, 1415-1430.
Walters, G. D., White, T. W., & Denney, D. (1991). The
Lifestyle
Criminality Screening Form: Preliminary data. Criminal Jus-
tice and Behavior, 18, 406-418.
Wolfgang, M. E., & Ferracuti, E. (1967). Subculture of
violence:
Towards an integrated theory of criminology. London, England:
Tavistock.
Available online at www.sciencedirect.com
ScienceDirect
Comprehensive Psychiatry 55 (2014) 489–496
www.elsevier.com/locate/comppsych
Mediators and moderators of functional impairment in adults
with
obsessive–compulsive disorder

Eric A. Storcha,b,⁎, Monica S. Wua,c, Brent J. Smalld, Erika A.
Crawforda, Adam B. Lewina,b,
Betty Hornga, Tanya K. Murphya,b
aDepartment of Pediatrics, University of South Florida, St.
Petersburg, FL 33701, USA
bDepartment of Psychiatry and Behavioral Neurosciences,
University of South Florida, St. Petersburg, FL 33701, USA
cDepartment of Psychology, University of South Florida, St.
dSchool of Aging Studies, University of South Florida, St.
Abstract
The current study examined correlates, moderators, and
mediators of functional impairment in 98 treatment-seeking
adults with
obsessive–compulsive disorder (OCD). Participants completed
or were administered measures assessing obsessive–compulsive
symptom
severity, functional impairment, resistance against symptoms,
interference due to obsessive–compulsive symptoms, depressive
symptoms,
insight, and anxiety sensitivity. Results indicated that all
factors, except insight into symptoms, were significantly
correlated with functional
impairment. The relationship between obsessive–compulsive
symptom severity and functional impairment was not moderated
by patient
insight, resistance against obsessive–compulsive symptoms, or
anxiety sensitivity. Mediational analyses indicated that
obsessive–compulsive
symptom severity mediated the relationship between anxiety
sensitivity and obsessive–compulsive related impairment.

Indeed, anxiety
sensitivity may play an important contributory role in
exacerbating impairment through increases in obsessive–
compulsive symptom
severity. Depressive symptoms mediated the relationship
between obsessive–compulsive symptom severity and
obsessive–compulsive
related impairment. Implications for assessment and treatment
are discussed.
© 2014 Elsevier Inc. All rights reserved.
Obsessive–compulsive disorder (OCD) is a debilitating
neuropsychiatric condition characterized by obsessions (i.e.,
recurrent and distressing thoughts, images, or impulses) and/
or compulsions (i.e., repetitive behaviors or mental acts
performed to reduce distress) [1]. Although the severity of
obsessive–compulsive symptoms is directly associated with
the degree of functional impairment experienced [2–5], this
relationship is not absolute; there are other variables that
contribute to understanding who is at greater risk of
compounded impairment and which mechanisms operate in
influencing impairment. Accordingly, this study extends the
literature by examining factors believed to be theoretically
relevant in understanding impairment among treatment-
seeking adults with OCD.
⁎ Corresponding author at: Department of Pediatrics, University
of
South Florida, 880 6th St. South, Box 460, St. Petersburg, FL
33701, USA.
Tel.: +1 727 767 8230.
E-mail address: [email protected] (E.A. Storch).
0010-440X/$ – see front matter © 2014 Elsevier Inc. All rights
reserved.
http://dx.doi.org/10.1016/j.comppsych.2013.10.014
To date, several studies have examined clinical charac-

teristics associated with functional impairment. In addition to
obsessive–compulsive symptom severity, ability to resist
and control obsessive–compulsive symptoms [2–4,6] and
co-occurring depressive and anxiety symptoms [5–8] have
been associated with functional impairment. Among children
and adolescents with OCD, insight predicted parent- and
child-rated functional impairment above and beyond obses-
sive–compulsive symptom severity [19]. In an effort to
understand potential mechanisms of impairment, one recent
study of adults with OCD found that depressive symptoms
and obsessive–compulsive symptom resistance/control me-
diated the relationship between obsessive–compulsive
symptom severity and functional impairment [6]. Although
informative, other variables may be relevant in understand-
ing why some individuals experience compounded impair-
ment beyond that which is conferred by the degree of
obsessive–compulsive symptom severity.
Anxiety sensitivity has emerged as an important variable in
understanding the development and maintenance of various
http://www.sciencedirect.com/science/journal/0010440X
http://www.sciencedirect.com/science/journal/0010440X
mailto:[email protected]
490 E.A. Storch et al. / Comprehensive Psychiatry 55 (2014)
489–496
anxiety disorders [9]. Defined as a fear of arousal- or anxiety-
related sensations which are misinterpreted by the individual
as harmful or dangerous [10], elevated anxiety sensitivity is
associated with difficulty experiencing and tolerating anxiety-

related sensations [9]. Conceptually, anxiety sensitivity may
relate to impairment by virtue of how someone with OCD
experiences his or her symptoms and associated distress. An
individual with high anxiety sensitivity may experience the
distress associated with his or her obsessive–compulsive
symptoms as unbearable and be more likely to engage in
rituals or active avoidance of anxiogenic triggers, thus
compounding impairment. Problematically, such behaviors
contribute to the increased potential for illness chronicity
through negative reinforcement (i.e., distress reduction) of
obsessive–compulsive symptoms.
A modest literature exists examining anxiety sensitivity
among adults with OCD. Anxiety sensitivity levels were
elevated in adults with OCD relative to non-clinical controls
[11–13], and were at comparable levels to adults with non-
OCD anxiety disorders [11]. There are a limited number of
examinations into the association between anxiety sensitivity
and obsessive–compulsive symptom severity. In 280 adults
with OCD, Calamari et al. [14] found that anxiety sensitivity
and obsessive–compulsive symptom severity were signifi-
cantly related. Wheaton et al. [15] demonstrated modest
associations between anxiety sensitivity and dimensional
ratings of obsessive–compulsive symptoms in a large non-
clinical sample of university students. Collectively, these
studies suggest that OCD caseness is linked to elevated
anxiety sensitivity relative to non-clinical samples, and may
be directly linked with obsessive–compulsive symptom
severity. However, these studies do not address the manner
in which anxiety sensitivity may contribute to functional
impairment, which has potential implications for the
conceptualization and care of individuals with OCD. First,
anxiety sensitivity may be one method through which
obsessive–compulsive symptom severity is exacerbated
and/or maintained. As stated, high anxiety severity may be
linked to greater ritualizing/avoidance and less symptom

resistance, contributing to impairment and sustained symp-
tomology. Second, anxiety sensitivity may be linked to a
more chronic symptom course. Individuals with high anxiety
sensitivity may be less likely to exhibit decreases in symptom
severity relative to those with lower anxiety sensitivity [16],
perhaps explaining, in part, the chronic nature of OCD in the
absence of treatment.
Beyond anxiety sensitivity, other variables may hold
relevance in understanding which individuals may experi-
ence compounded impairment. Insight into the degree to
which obsessive–compulsive symptoms are recognized by
the person as absurd, excessive, and senseless has been
linked to obsessive–compulsive symptom severity and
functional impairment in past studies of adults [17] and
children with OCD [18,19]. Additionally, individuals with
poor insight into their OCD symptomology have exhibited
more complicated clinical presentations and poorer treatment
response when compared to individuals with higher insight
[17,20]. It is reasonable to consider that insight may
moderate the relationship between obsessive–compulsive
symptom severity and impairment such that those with poor
insight may be more clinically complex and be less able to
function effectively or actively challenge symptoms. Simi-
larly, symptom resistance is also hypothesized to be relevant
in understanding who is at risk for greater impairment in that
those who actively try to challenge their symptoms would be
less likely to experience OCD-related impairment. Indeed,
the lower levels of resistance against obsessive–compulsive
symptoms have been linked with increased obsessive–
compulsive symptom severity [21], as well as decreased
functioning and higher impairment [6,22].
In the present study, we examine correlates, moderators,
and mediators of functional impairment in adults with OCD.
Our specific study questions and hypotheses were as

follows. First, what are the relations among domains of
functional impairment and obsessive–compulsive symptom
severity, symptom resistance, interference due to obsessive–
compulsive symptoms, anxiety sensitivity, depressive
symptoms, and insight? We expected that the varied
domains of functional impairment would be directly
associated with obsessive–compulsive symptom severity,
interference due to obsessive–compulsive symptoms, anx-
iety sensitivity, and depressive symptoms, and inversely
related to symptom resistance and insight. Second, we
examined the extent to which insight, resistance against
obsessive–compulsive symptoms, and anxiety sensitivity
moderated the relationship between obsessive–compulsive
symptom severity and OCD-related impairment. We
expected that each variable would moderate this association
such that the relationship between obsessive–compulsive
symptom severity and OCD-related impairment would be
more robust for those with lower insight and symptom
resistance, and higher anxiety sensitivity. Third, would the
relationship between anxiety sensitivity and OCD-related
functional impairment be mediated by obsessive–compul-
sive symptom severity? We predicted that as anxiety
sensitivity increased, obsessive–compulsive symptom se-
verity would increase, which would be positively associated
with functional impairment. Finally, in an effort to replicate
Storch et al. (2009), would the relationship between
obsessive–compulsive symptom severity and OCD-related
functional impairment be mediated by depressive symp-
toms? We expected that as obsessive–compulsive symptom
severity increased, depressive symptoms would increase,
which would be directly associated with augmented
functional impairment.
1. Method
1.1. Participants and procedures

Participants included 98 adults with a primary diagnosis
of OCD that presented to an OCD specialty center to initiate
491E.A. Storch et al. / Comprehensive Psychiatry 55 (2014)
489–496
cognitive–behavioral treatment (Table 1). Diagnoses were
established using best estimate diagnostic procedures in
which consensus between two experienced clinicians (one of
whom interviewed the participant in an unstructured
assessment) was required on the primary diagnosis and the
presence of comorbid diagnoses. Clinicians used all
available information to ascertain an accurate diagnostic
profile by including clinical information gleaned from an
unstructured clinical interview, reviewing participants’
completed measures as part of this study, and examining
past clinical records. Participants were excluded in the
absence of 100% agreement for the primary or comorbid
diagnoses, or if diagnosed with psychosis, mental retarda-
tion, or bipolar disorder. The participants were between 18
and 72 years of age (M = 33.1, SD = 13.53) and were 57%
female. The sample was 84% Caucasian, 10% Hispanic, 2%
African American, 2% Asian, and 2% classified as ‘other.’
Common comorbid diagnoses included depression (57%),
generalized anxiety disorder (30%), impulse control
disorder-not otherwise specified (12%), and social phobia
(10%). Seventy-nine participants (81%) reported currently
taking psychotropic medication.
All study procedures were approved by the local
institutional review board. Following a regularly scheduled
clinic visit, patients appropriate for the study were
approached by a member of the research team who was
otherwise uninvolved in the individual’s care to review the
study; interested participants provided their written consent.

Participants then were administered the Yale–Brown
Obsessive Compulsive Scale (Y-BOCS) [23,24] by a trained
rater and thereafter completed self-report measures. Based
on the Y-BOCS interview, the clinician rated the National
Institutes of Mental Health Global Obsessive Compulsive
Table 1
Demographic characteristics of the study sample (n = 98).
Variable
Gender (Male/Female) 41 Males (42%)/57 Females (58%)
Age (Years) M = 33.10, SD = 13.53
Range = 18 to 72 years
Race/Ethnicity
Caucasian n = 82 (84%)
Hispanic n = 10 (10%)
African–American n = 2 (2%)
Asian n = 2 (2%)
Other n = 2 (2%)
Comorbid Diagnosesa
Depressive disordersb n = 56 (57%)
Generalized anxiety disorder n = 29 (30%)
Impulse control disorder not
otherwise specified
n = 12 (12%)
Social phobia n = 10 (10%)
Taking Psychotropic Medication n = 79 (81%)
a The four most common comorbidities are listed in the table.
Comorbid
diagnoses occurring with less frequency are not reported.

b Depressive disorders included those diagnosed with major
depression,
dysthymia, and depressive disorder not otherwise specified.
Scale (NIMH-GOCS) [25]. All independent evaluators
underwent extensive training with the first author in the
administration of Y-BOCS and NIMH-GOCS.
1.2. Measures
1.2.1. Yale–Brown Obsessive Compulsive Scale
(Y-BOCS) [23,24]
The Y-BOCS is a clinician-administered semi-struc-
tured interview that assesses the presence and severity of
obsessive–compulsive symptoms. Insight into obsessive–
compulsive symptoms (item #11 on the Y-BOCS;
possible score ranges from 0 to 4), interference due to
obsessive–compulsive symptoms (sum of items #1, 2, 3,
6, 7, and 8; possible scores ranging from 0 to 24),
resistance against obsessive–compulsive symptoms (sum
of items #4, 5, 9, and 10; possible scores ranging from
0 to 16), and Severity Scale total score (possible scores
ranging from 0 to 40) were all gathered through this
measure. Higher scores on insight indicate poorer insight,
higher scores on interference indicate higher interference,
and higher scores on resistance indicate greater difficulty
with resistance. The Y-BOCS has demonstrated excellent
psychometric properties with regard to reliability and
validity [26–28].
1.2.2. Sheehan Disability Scale (SDS) [29]
The SDS is a 3-item self-report questionnaire that
assesses the level of impairment experienced due to
obsessive–compulsive symptoms in social, occupational,
and family life. The impairment scores for each domain

(possible scores range from 0 to 10) and the total score were
used (possible scores range from 0 to 30), and higher scores
indicate higher impairment. The SDS has shown good
psychometric properties with regard to internal consistency
and validity [30,31].
1.2.3. National Institutes of Mental Health Global
Obsessive Compulsive Scale (NIMH-GOCS) [25]
The NIMH-GOCS is a one-item clinician-administered
measure that assesses the severity of obsessive–compulsive
symptoms on a scale from 1 to 15, with higher scores
indicating more severe obsessive–compulsive symptoms.
1.2.4. Beck Depression Inventory-Second Edition (BDI-II) [32]
The BDI-II is a 21-item self-report questionnaire that
assesses the presence and severity of depressive symptoms in
the past week. The total score was used (possible scores
range from 0 to 63), and higher scores on the BDI-II indicate
more depressive symptoms. The BDI-II has demonstrated
adequate validity and reliability, including discriminant and
construct validity, internal consistency, and test-retest
reliability [32,33].
1.2.5. Anxiety Sensitivity Index-Revised (ASI-R) [34]
The ASI-R is a 36-item self-report questionnaire that
assesses the respondent’s fear of anxiety and its respective
sensations, due to the perceived negative consequences of
Table 2
Correlation coefficients, means, standard deviations, and ranges
for all study measures (n = 98).

1 2 3 4 5 6 7 8 9 10 11
1. SDS Total Score 0.79⁎⁎⁎ 0.88⁎⁎⁎ 0.83⁎⁎⁎ 0.65⁎⁎⁎ 0.17
0.76⁎⁎⁎ 0.31⁎⁎ 0.70⁎⁎⁎ 0.72⁎⁎⁎ 0.26⁎
2. SDS Work 0.55⁎⁎⁎ 0.45⁎⁎⁎ 0.57⁎⁎⁎ 0.11 0.65⁎⁎⁎ 0.22⁎
0.58⁎⁎⁎ 0.54⁎⁎⁎ 0.14
3. SDS Social 0.65⁎⁎⁎ 0.56⁎⁎⁎ 0.19 0.64⁎⁎⁎ 0.33⁎⁎ 0.62⁎⁎⁎
0.68⁎⁎⁎ 0.30⁎⁎
4. SDS Family/Home 0.51⁎⁎⁎ 0.13 0.62⁎⁎⁎ 0.23⁎ 0.56⁎⁎⁎
0.58⁎⁎⁎ 0.20⁎
5. NIMH GOCSa 0.21⁎ 0.86⁎⁎⁎ 0.47⁎⁎⁎ 0.84⁎⁎⁎ 0.51⁎⁎⁎ 0.26⁎
6. Y-BOCS Insight 0.17 0.31⁎⁎ 0.27⁎⁎ 0.22⁎ 0.08
7. Y-BOCS Interference 0.38⁎⁎⁎ 0.90⁎⁎⁎ 0.59⁎⁎⁎ 0.30⁎⁎
8. Y-BOCS Resistance 0.74⁎⁎⁎ 0.30⁎⁎ 0.18
9. Y-BOCS Total Score 0.57⁎⁎⁎ 0.30⁎⁎
10. BDI-II Total Scorea 0.37⁎⁎⁎
11. ASI-R Total Scorea
Mean 19.26 6.27 6.36 6.63 9.26 0.62 15.00 9.44 24.44 21.93
47.45
SD 7.16 2.79 3.01 2.76 2.10 0.86 3.73 2.35 5.10 11.31 28.91
Range 0–30 0–10 0–10 0–10 5–13 0–3 7–24 4–16 13–36 0–49
0–122
⁎ p b 0.05.
⁎⁎ p b 0.01.
⁎⁎⁎ p b 0.001.
a Indicates sporadic missing data. NIMH GOCS (n = 95), BDI-II

(n = 97), ASI-R (n = 97).
489–496
the feelings. The total score was used (possible scores range
from 0 to 144), with higher scores indicating greater anxiety
sensitivity. The ASI-R has been shown to be psychometri-
cally sound as demonstrated through high internal consis-
tency and reliability [35]. The ASI-R also contains six
factorially derived subscales, investigating: (1) fear of
cardiovascular symptoms, (2) fear of respiratory symptoms,
(3) fear of gastrointestinal symptoms, (4) fear of publicly
observable anxiety reactions, (5) fear of dissociative and
neurological symptoms, and (6) fear of cognitive dyscontrol.
1.3. Statistical analyses
Prior to analysis, variables were evaluated for the
presence of outliers and multicollinearity, and distributional
properties were examined. For the moderation effects in
regression, analyses were conducted using the MODPROBE
macro through SPSS as described by Hayes and Matthes
[36]. The mediation analyses were computed in Mplus [37]
with bootstrapped standard errors for the direct and indirect
effects. This method provides the same basic information as
the classic Baron and Kenny [38] approach, but provides a
specific test for the mediated effect and increases statistical
power through the bootstrapped resampling [39]. For the
correlations, there were sporadic missing values and the
pairwise sample sizes are reported. For the moderation and
mediation analyses, multiple imputation using SAS Proc MI
[40,41] and maximum likelihood estimates using Mplus
were employed, respectively.
2. Results
2.1. Descriptive statistics and correlations

Descriptive statistics (i.e., mean, standard deviation,
and range) were calculated for all study variables (see
Table 2). Pearson correlation coefficients were calculated
for all study variables and statistical significance was
adjusted for multiple comparisons using the Holm-
Bonferroni correction [42]. Regardless of examining
corrected or non-corrected correlations, all variables
remained significantly correlated with the SDS total score
(OCD-related impairment), except for insight into obses-
sive–compulsive symptoms. Specifically, the SDS total
score demonstrated a strong positive correlation with
obsessive–compulsive symptom severity (NIMH GOCS,
Y-BOCS Total score), interference (Y- BOCS Interfer-
ence), and depressive symptoms (BDI-II), a moderate
positive correlation with resistance against obsessive–
compulsive symptoms (Y-BOCS Resistance), and a weak
positive correlation with anxiety sensitivity (ASI-R).
Impairment in work possessed a strong positive correlation
with obsessive–compulsive symptom severity, interference,
and depressive symptoms, and a weak positive correlation
with resistance against obsessive–compulsive symptoms
and anxiety sensitivity. Social impairment exhibited a
strong positive correlation with obsessive–compulsive
symptom severity, interference, and depressive symptoms,
and a moderate positive correlation with resistance
against obsessive–compulsive symptoms and anxiety
sensitivity. Lastly, impairment in family life or home
responsibilities demonstrated a strong positive correlation
with obsessive–compulsive symptom severity, interference,
and depressive symptoms, and a weak positive correlation
with resistance against obsessive–compulsive symptoms
and anxiety sensitivity. No impairment domain possessed
a statistically significant correlation with insight into
obsessive–compulsive symptoms.

To explore potentially differential relationships between
specific components of anxiety sensitivity and obsessive–
compulsive symptom severity, Pearson correlation coeffi-
cients were also run to examine the relationship between
489–496
each subscale on the ASI-R and the Y-BOCS Total score
(Table 3).1 Fear of cardiovascular symptoms (subscale 1)
and fear of gastrointestinal symptoms (subscale 3) did
not demonstrate statistically significant correlations with
obsessive–compulsive symptom severity. Fear of respiratory
symptoms (subscale 2), fear of publicly observable anxiety
reactions (subscale 4), fear of dissociative and neurological
symptoms (subscale 5), and fear of cognitive dyscontrol
(subscale 6) all shared weak to moderate relationships with
obsessive–compulsive symptom severity.
2.2. Moderation analyses
Insight into obsessive–compulsive symptoms, resistance
against obsessive–compulsive symptoms, and anxiety sen-
sitivity were not significant moderators of the relationship
between obsessive–compulsive symptom severity and
OCD-related impairment. In testing insight, resistance, and
anxiety sensitivity as potential moderators, NIMH GOCS
was a significant predictor of OCD-related impairment (β =
2.04, p b 0.0001; β = 2.05, p b 0.0001; β = 1.89,
p b 0.0001), respectively. However, insight was not a
significant predictor of OCD-related impairment (β = 0.60,
p = 0.40), and neither was the interaction term for NIMH
GOCS and insight (β = −0.31, p = 0.41). Additionally,
neither resistance (β = 0.05, p = 0.87) nor the interaction
term between NIMH GOCS and resistance was a significant

predictor for OCD-related impairment (β = −0.20,
p = 0.09). Lastly, anxiety sensitivity was not a significant
predictor of OCD-related impairment (β = 0.04, p = 0.09),
nor was the interaction term between NIMH GOCS and
anxiety sensitivity (β = −0.01, p = 0.17).
2.3. Mediation analyses2
Obsessive–compulsive symptom severity (NIMH
GOCS) was tested as a potential mediator between anxiety
sensitivity (ASI-R) and OCD-related impairment (SDS). The
indirect effect was statistically significant (β = 0.03, SE =
0.01, p b 0.05) suggesting that the relationship between
anxiety sensitivity and OCD-related impairment was
explained by symptom severity, with higher anxiety
sensitivity scores associated with higher obsessive–
compulsive symptom severity scores (β = 0.02, SE = 0.01,
p b 0.05), and higher obsessive–compulsive symptom
severity scores associated with higher OCD-related impair-
ment (β = 2.11, SE = 0.26, p b 0.001). Finally, the direct
effect between anxiety sensitivity and OCD-related impair-
1 Correlations with each ASI-R subscale remained statistically
significant with highly comparable strengths when the
correlations were
re-run with NIMH GOCS as the measure of OCD symptom
severity.
2 All mediating effects remained the same when the mediation
analyses
were re-run with Y-BOCS Total score as the measure of OCD
symptom
severity. As such, NIMH GOCS was utilized as the metric for
OCD
symptom severity throughout the moderation and mediation
analyses to
maintain consistency.

ment was no longer statistically significant after the mediator
was added to the model (β = 0.03, SE = 0.02, p = 0.17).
Depressive symptoms were tested as a potential mediator
between obsessive–compulsive symptom severity (NIMH
GOCS) and OCD-related impairment (SDS). The results
demonstrated that depressive symptoms were a statistically
significant mediator, with an indirect effect of 0.94 (SE =
0.19, p b 0.001) suggesting that the relationship between
symptom severity and OCD-related impairment was
explained by depressive symptoms, with higher scores on
obsessive–compulsive symptom severity associated with
higher depressive symptoms (β = 2.81, SE = 0.43,
p b 0.001), and higher scores on depressive symptoms
being associated with higher OCD-related impairment (β =
0.33, SE = 0.05, p b 0.001). Although the indirect effect
was statistically significant, the direct effect of obsessive–
compulsive symptom severity and OCD-related impairment
was also statistically significant (β = 1.26, SE = 0.27,
p b 0.001).
3. Discussion
We report on correlates, moderators, and mediators of
functional impairment in adults with OCD. As expected,
domains of impairment were strongly related to obsessive–
compulsive symptom severity and interference, and mod-
estly associated with symptom resistance and depressive
symptoms. Relations of this magnitude are reflective of the
impairing and distressing nature associated with obsessive–
compulsive symptom severity. Indeed, OCD distinguishes
itself from other anxiety disorders in terms of the degree of
impairment [43], which contributes to it being listed as a
leading cause of disability [44,45]. Weak associations were
found between impairment and anxiety sensitivity and
insight, which suggest that OCD caseness confers greater
risk for impairment regardless of insight or anxiety

sensitivity, such that more severe presentations are not
necessarily linked with limited insight. It may also be that
those with poor insight have similarly limited awareness into
their own impairment. Additionally, only certain subscales
on the assay of anxiety sensitivity were significantly
correlated with obsessive–compulsive symptom severity,
which may suggest that fear of cardiovascular and
gastrointestinal symptoms may be less related to obses-
sive–compulsive symptom severity when compared to other
domains of anxiety sensitivity. Furthermore, the strength of
the statistically significant correlations with obsessive–
compulsive symptom severity was highly comparable not
only across the ASI-R subscales, but also with the ASI-R
total score. As such, it appears that the fears of respiratory
symptoms, publicly observable anxiety reactions, dissocia-
tive and neurological symptoms, and cognitive dyscontrol do
not seem to show differential associations with obsessive–
compulsive symptom severity when compared to one
another, nor when compared to anxiety sensitivity as a
Table 3
Correlation coefficients, means, standard deviations, and ranges
for ASI-R subscales and OCD symptom severity (n = 98).
1 2 3 4 5 6 7
1. Y-BOCS Total Score 0.13 0.29⁎⁎ 0.12 0.29⁎⁎ 0.27⁎⁎ 0.28⁎⁎
2. ASI-R S1: Fear of Cardiovascular Symptomsa 0.59⁎⁎⁎
0.59⁎⁎⁎ 0.45⁎⁎⁎ 0.69⁎⁎⁎ 0.36⁎⁎⁎
3. ASI-R S2: Fear of Respiratory Symptoms 0.43⁎⁎⁎ 0.53⁎⁎⁎
0.75⁎⁎⁎ 0.46⁎⁎⁎

4. ASI-R S3: Fear of Gastrointestinal Symptomsa 0.43⁎⁎⁎
0.64⁎⁎⁎ 0.20⁎
5. ASI-R S4: Fear of Publicly Observable Anxiety Symptoms
0.64⁎⁎⁎ 0.48⁎⁎⁎
6. ASI-R S5: Fear of Dissociative and Neurological Symptomsa
0.65⁎⁎⁎
7. ASI-R S6: Fear of Cognitive Dyscontrola
Mean 24.44 6.57 9.10 3.32 14.40 7.89 6.32
SD 5.10 6.18 7.77 4.19 7.28 5.58 5.94
Range 13–36 0–23 0–28 0–16 0–32 0–23 0–20
⁎ p b 0.05.
⁎⁎ p b 0.01.
⁎⁎⁎ p b 0.001.
a Indicates sporadic missing data. ASI-R Subscales 1, 3, 5, and
6 (n = 97).
489–496
construct in general. These results suggest that relatively
equal consideration should be given to the respective
components of anxiety sensitivity when considering their
relationship with OCD symptom severity.
One of the primary findings of interest was that
obsessive–compulsive symptom severity mediated the
relationship between anxiety sensitivity and OCD-related
impairment. Stated differently, as anxiety sensitivity in-
creased, obsessive–compulsive symptom severity increased,
which was directly associated with augmented functional
impairment. This is consistent with past findings implicating
the role of anxiety sensitivity and symptom severity [14,16],

and provides a potential mechanism through which anxiety
sensitivity exacerbates symptom severity and ultimately
functional impairment. Among individuals presenting with
elevated anxiety sensitivity, this may trigger more severe
clinical presentations characterized by extreme distress,
decreased distress tolerance, and difficulty resisting/control-
ling obsessive–compulsive symptoms, which translate into
greater functional impairment relative to those with lower
levels of anxiety sensitivity. Speculatively, anxiety sensitiv-
ity may be one variable associated with attenuated
homework compliance in exposure and response prevention
treatment. That is, individuals with high anxiety sensitivity
may be less likely to independently engage in homework
tasks as they may find the experience too distressing.
Problematically, homework compliance is a key element of
psychotherapeutic treatment in which poor compliance is
associated with reduced response [46,47].
Similar to past findings [6], the relationship between
obsessive–compulsive symptom severity and OCD-related
functional impairment was mediated by depressive symp-
toms, such that as obsessive–compulsive symptom severity
increased, depressive symptoms correspondingly increased,
contributing to greater functional impairment. Studies
document the temporal association between obsessive–
compulsive symptom onset and a corresponding increase
in depressive symptoms [48], which is believed to be
secondary to the distressing and time intensive nature of
obsessive–compulsive symptoms. The contribution of
depressive symptoms to the clinical picture of OCD may
increase risk for impairment by virtue of additional
psychiatric morbidity or the interactional nature of having
two impairing problems. Correspondingly, there is evidence
that reducing obsessive–compulsive symptoms through
evidence-based OCD interventions translates into improve-
ments in depressive symptomology [49,50]. Thus, treating

obsessive–compulsive symptoms may be one method of
improving comorbid depressive symptoms that are function-
ally related to OCD. However, this approach may not
translate to all patients; modular interventions that are
personalized to individual patient characteristics such as
comorbid depression would be well-suited for this cohort.
Interestingly, insight into obsessive–compulsive symp-
toms, resistance against obsessive–compulsive symptoms,
and anxiety sensitivity did not significantly moderate the
relationship between obsessive–compulsive symptom se-
verity and OCD-related functional impairment. Regarding
insight, past studies, including this one, have found relatively
weak associations between symptom insight and obsessive–
compulsive symptom severity [51–53]. This suggests that
insight may not be the driving force in understanding why
some people are more impaired than others. It may be that
people with very limited insight are not aware of the degree
to which they are impacted by their symptoms; alternatively,
they may have family members who are providing
significant accommodation with the goal of reducing
functional impairment. However, the finding that insight
was not a significant mediator may also be attributed to a
range restriction problem with the variable; upon examina-
tion of the mean and standard deviation for the insight
variable within this sample, a truncated range was indeed
observed and could have been the source of precluding
support for the hypothesis. It is somewhat surprising that
resistance against obsessive–compulsive symptoms did not
moderate the association between symptom severity and
impairment, as it was expected that those who resist less
would be more likely to experience impairment as their

489–496
symptoms increased. Although there are clearly merits of
resisting symptoms, it may be that symptom resistance yields
differential effects across people; that is, some people may
experience some cathartic benefits associated with reduced
impairment, while others may not experience the same
benefits through the emotional and tangible efforts required
for successful resistance. It may be that anxiety sensitivity
does not function as a moderator but rather exerts its effect
through mediation by contributing to obsessive–compulsive
severity, which in turn compounds functional impairment.
Several limitations to this study should be noted. First, the
generalizability of the results may be restricted due to
demographics of the study sample, as participants were
primarily Caucasian and seeking treatment for their OCD.
Second, the study was cross-sectional, limiting the ability to
establish causality. Third, our assessment of insight relied on
one clinician administered item and may not be as sensitive
and/or comprehensive as using a tailored measure of insight
(e.g., BrownAssessment ofBeliefs) or other related constructs,
such as overvalued ideas (e.g., Overvalued Ideas Scale).
Lastly, other variables not examined in the present study (e.g.,
cognitive variables) may be relevant in understanding the
relationship between obsessive–compulsive symptoms and
OCD-related impairment.
Within these limitations, this study has important implica-
tions for the care of individuals with OCD. First, these data
speak to the importance of assessing anxiety sensitivity in
addition to more traditional constructs (e.g., comorbidity,
insight, etc.). Given that anxiety sensitivity is linked to elevated
obsessive–compulsive symptom severity, which in turn is
associated with increased functional impairment, evaluating
the patient’s level of anxiety sensitivity may help identify
potential risk factors for OCD-related impairment. Addressing

heightened anxiety sensitivity in treatment may also help with
improved tolerability and quicker habituation to anxiety-
producing situations, aiding in the process of exposure and
response prevention. However, it remains unclear if anxiety
sensitivity improves through standard therapies (e.g., cognitive
behavioral therapy, antidepressant medications) or requires
alternative, adjunctive approaches (e.g., interoceptive expo-
sure) [54]. Second, depressive symptoms should correspond-
ingly be assessed; these symptoms may serve as an underlying
mechanism that is influenced by obsessive–compulsive
symptom severity, ultimately contributing to functional
impairment. As such, it is important to monitor the severity
of depressive symptoms as treatment progresses, examining
any changes (or lack thereof) secondary to the alleviation of
obsessive–compulsive symptoms. Given the influence of
depressive symptomology on functional impairment, any
substantial symptoms remaining after OCD treatment may
warrant more targeted interventions for the depression.
Collectively, it is important to consider variables that
contribute to OCD-related disability, as it allows clinicians to
better predict risk factors and utilize targeted interventions to
decrease functional impairment. Given the association with
decreased functioning and poorer treatment response [55],
prudent identification and interventions for these constructs are
hoped to help improve the prognosis of individuals with OCD.
References
[1] American Psychiatric Association. Diagnostic and statistical
manual of
mental disorders. 5th ed. Washington, DC: American
Psychiatric
Association; 2013.
[2] Bystritsky A, Liberman RP, Hwang S, Wallace CJ, Vapnik
T,

Maindment K, et al. Social functioning and quality of life
comparisons
between obsessive–compulsive and schizophrenic disorders.
Depress
Anxiety 2001;14(4):214-8.
[3] Bobes J, Gonzalez MP, BascaranMT, Arango C, Saiz PA,
BousonoM.
Quality of life and disability in patients with obsessive–
compulsive
disorder. Eur Psychiatry 2001;16(4):239-45.
[4] GrabeHJ,Meyer C,HapkeU, RumpfHJ, Freyberger HJ,
Dilling H, et al.
Prevalence, quality of life and psychosocial function in
obsessive–
compulsive disorder and subclinical obsessive–compulsive
disorder in
Northern Germany. Eur Arch Psychiatry Clin Neurosci
2000;250(5):
262-8.
[5] Huppert JD, Simpson HB, Nissenson KJ, Liebowitz MR, Foa
EB.
Quality of life and functional impairment in obsessive–
compulsive
disorder: a comparison of patients with and without
comorbidity,
patients in remission, and healthy controls. Depress Anxiety
2009;
26(1):39-45.
[6] Storch EA, Abramowitz JS, Keeley M. Correlates and
mediators of
functional disability in obsessive–compulsive disorder. Depress
Anxiety

Exercise 4 Cardiovascular FitnessThis exercise describes thre.docx

Exercise 4 Cardiovascular FitnessThis exercise describes thre.docx

Recommended

Recommended

More Related Content

Similar to Exercise 4 Cardiovascular FitnessThis exercise describes thre.docx

Similar to Exercise 4 Cardiovascular FitnessThis exercise describes thre.docx (20)

More from rhetttrevannion

More from rhetttrevannion (20)

Recently uploaded

Recently uploaded (20)

Exercise 4 Cardiovascular FitnessThis exercise describes thre.docx