Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2011 Validity of the Addiction Prone Personality Scale Sandi Sage Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected]
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The Validity Of The Addiction Prone Personality Scale
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Florida State University Libraries
Electronic Theses, Treatises and Dissertations The Graduate School
2011
Validity of the Addiction Prone PersonalityScaleSandi Sage
Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected]
1. Demographic Data………………………………………………………………………..65 2. Internal consistency of the APP Scale………………………………………....................76 3. Item total correlations of the APP for the combined groups……………………………..77 4. Initial Eigenvalues based on Principal Components Analysis of the combined groups………………………………………………………………………….78 5. Rotated Components Analysis using Varimax Rotation for combined groups……………………………………………………………………………………..80 6. Correlations between APP total and SASSI-3 scales total score for combined groups………………………………………………………………..................82 7. Model summary with SASSI-3 score entered first followed by the APP as predictors of probability of substance dependency (n=199)……………………...83 8. ANOVA comparing clinical and student populations using the APP as the dependent variable………………………………………………………………….84 9. Univariate tests of SASSI-3 scales to differentiate clinical and and student populations……………………………………………………………………86
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ABSTRACT
Young adults, age 18-30 years are in the highest risk group for developing substance use
disorders (SUDs) and these disorders are associated with a myriad of negative
consequences. Researchers in the field of psychology studying SUDs and personality
variables have determined that specific personality traits tend to be associated with and
affect the type and severity of SUD’s. There appear to be 2 clusters of substance abusers:
those high in Psychoticism or “P” traits such as impulsivity, sensation-seeking, disinhibition,
anti-social behaviors and those high in Neuroticism or “N” traits such as internalizing,
depressive tendencies, negative views and anxiety.
The Addiction Prone Personality scale (APP) (Barnes et al., 2000) was developed as a
measure of personality vulnerability to SUD’s. Given that the APP is a relatively new scale
and that the research is limited, further research investigating the scale’s psychometric
properties seems justified. The present study examined the construct validity of the APP with
5 sub-validity studies to examine internal consistency/reliability, factor validity, convergent
validity, incremental validity, and criterion validity. This study employed a young adult
population, ages 18-30, drawn from two samples: a college student sample and a clinical
sample of DUI offenders referred for assessment and psycho-educational treatment.
Results were mixed in that the APP demonstrated strong internal consistency with the
clinical group, but weak internal consistency for the student and combined groups. The
factor analysis of the APP revealed three underlying subscales which measure constructs
consistent with previous research. However, there was no underlying unidimensional scale.
Therefore the total score is impossible to interpret. Further, while the APP had moderate
convergent validity with the SASSI-3, there was no significant incremental or discriminant
validity with these samples using the SASSI-3. Given the limited psychometric properties, the
results suggest that the APP in its present form would not be appropriate for use with
individuals in predicting addiction proneness. The results are discussed in terms of previous
research and recommendations for future research into the construct of addiction proneness
are offered.
1
CHAPTER 1
INTRODUCTION
Statement of the Problem
Substance use disorders (SUDs) are one of the most important contributors to preventable
morbidity and mortality in America and among the most difficult public health challenges
Family vs. Control Measure (FAM); Correctional (COR) and Random Answering (RAP). Based
upon the configuration of scales, decision rules are used to measure the degree to which the
individual’s response patterns are similar to persons with substance use disorders.
In a study analyzing psychometric properties of the SASSI-3 (Lazowski et al., 1998), the
sensitivity and specificity of the instrument were both shown to be above 90%. The predictive
utility and accuracy of SASSI-3 classifications are not significantly affected by demographic
variables or by the level of adjustment and functioning Global Assessment of Functioning (GAF)
although the authors point out that the data were obtained from clinical settings and that the level
of accuracy might not generalize to other settings. The SASSI-3 was found to be related to
indexes of substance misuse and to other screening measures. Findings also indicated that
respondents who were classified as test positive on the SASSI-3 had higher mean scores on other
substance-abuse screening measures than did respondents who tested negative on the SASSI-3.
Together, these findings provide evidence for convergent validity. Some screening instruments
are based exclusively on respondents’ self- reports of symptoms of substance dependence (e.g.
AAS, MAST) whereas other consists exclusively of non-face-valid items (e.g. APS, MAC-R). In
addition to face-valid items, the SASSI-3 also includes subtle items in order to reduce the overall
error rate and improve classification. Since the goal is early identification of individuals who
may be substance dependent and who may not be able to acknowledge relevant symptoms,
screening instruments such as the SASSI-3 are of particular value.
Piazza et al., (2000) in a review of screening instruments for SUD’s, report that the SASSI-3 is
an efficient merger of two types of screens: logically derived (self- report) and empirically
derived. The advantages of the SASSI-3 are increased validity and reliability due to ability to
detect deception through the inclusion of subtle scales, and defensive scales that can be
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compared to face valid items.
Laux et al., (2005) investigated the SASSI-3’s psychometric capabilities in a college student
sample. Findings indicate that the SASSI-3’s psychometric properties are equal to or exceed
those of the Michigan Alcoholism Screening Test (MAST; Selzer, 1971), the CAGE (Ewing,
1984) and the MacAndrew Alcoholism Scale-Revised (MacAndrew, 1965). The SASSI-3’s
overall classification system and the specific FVA (face valid alcohol) subscale appear to have
strong test-retest stability, internal consistency and item-to-scale agreement. The FVA subscale
emerged as a unitary measure of alcohol use disorders. The FVA alpha (.93) was similar to an
alpha reported in another college student sample (.89; Myerholtz & Rosenberg, 1998). The
authors report that the SASSI-3’s strengths over other screens are its use of direct, indirect and
combined approaches to screening for substance use, its reported ability to screen for alcohol and
other drugs of abuse and the use of a battery of subscales to provide clinicians with a variety of
data for screening and treatment planning purposes.
In contrast, Clements (2002) found the SASSI-3 to have a much lower sensitivity (.65) and
clinical utility in a college student population. The author notes that lowering the cut off scores
for subscales, especially FVA significantly enhance the sensitivity rate (.89). This study raises
questions about the usefulness of the subtle items of the SASSI-3 in identifying alcohol
dependence in a college population and provides additional evidence for the need to examine this
instrument using this population.
Psychometric properties of the SASSI-3
The SASSI-3 was developed to meet human service practitioners need for an addictions
screening tool that does not rely on clients to be completely forthright in reporting relevant
behaviors (Miller & Lazowski, 1999). To that end, clinicians’ diagnoses regarding the presence
or absence of substance use disorders served as the criterion variable in validity analyses for
SASSI-3 with the provision that all such diagnoses also be based on DSM-IV (1994) symptom
criteria for diagnosing SUD’s. Thus, individuals who test positive on the SASSI-3 are likely to
be diagnosed as having a substance use disorder.
The clinical data set used to formulate and examine aspects of the SASSI-3 consisted of over
2000 respondents. Clinicians in service settings throughout the U.S. including addiction
treatment centers, general psychiatric hospitals, a dual diagnosis hospital, a vocational
rehabilitation program and a sex offender treatment program provided ninety-seven percent of
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the respondents. The remaining 3% were prisoners in a correctional facility or research subjects
recruited because they had a family history of alcohol abuse. The decision rules were formulated
a cross-validated on a sub-set of these cases (n=839).
Reliability
Two-week test-retest stability coefficients obtained with a sample of 40 respondents range
from .92 to 1.00 indicating high short-term stability. The SASSI-3 overall alpha coefficient is
.93; face valid alcohol .93 and face valid other drug .95. Alpha coefficients for the other
subscales of the SASSI-3 range from .27 to .79. The authors of the SASSI-3 note that the
coefficient alpha statistic is not necessarily a primary consideration for the scales of the SASSI-3
since they were not designed to be unidimensional in nature (Lazowski et al., 1998).
Validity
A study of the SASSI-3 classification accuracy found that it correctly identified 94.6% of the
people who were diagnosed as having a substance use disorder and correctly identified 93.2% of
those who were diagnosed as not having a substance use disorder. In a discriminant function
analysis in which all the items were entered as predictor variables of the presence or absence of a
substance use disorder revealed a maximum correct classification rate of 97%. When the SASSI-
3 decision rules were tested on the cross-validation sample, results indicated an overall accuracy
of 93.6% (Lazowski et al., 1998).
The DEF or defensiveness scale can be used to identify individuals who respond to the
instrument in a defensive or guarded manner. DEF scale items were selected to discriminate
between substance-dependent individuals who completed the instrument under standard
instruction and those who were given instruction to try to hide any sign of substance abuse.
Therefore, scores on the DEF scale can be used as an index of defensiveness. Classification
accuracy was 94.9% for cases where participants’ DEF scores were within one standard
deviation of the normative sample DEF scale mean (scores of 7 or less). When DEF scores were
elevated (DEF scores of 8 or more) results indicated an overall classification accuracy rate of
85.2%. It appears that the SASSI-3 is robust to variation in defensiveness. The inclusion of
subtle items and the research on defensive responding enable SASSI-3 classifications to be fairly
accurate (from 83% to 91%) even when DEF scores are somewhat elevated (8 or 9) and
somewhat less accurate only at extremely high levels of DEF (10 or 11).
The SASSI-3 includes both face valid and subtle items. The overall accuracy based
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exclusively on face valid scales is 79% which is a 14.9% loss relative to the 93.9% overall
accuracy obtained when using the full set of decision rules including the subtle scales.
The positive predictive power of the face valid scales is 99.6%. The negative predictive power
of the face valid scales is only 49.6%. Half of the individuals who did not acknowledge
significant substance abuse on the face valid scales were found in clinical assessments to have a
substance use disorder. The classification scheme based exclusively on face valid scales failed to
identify 20% of those diagnosed with a substance use disorder who were accurately identified
when using the subtle scales in the full set of decision rules. Sensitivity was 93.9% with the full
set of decision rules but fell to 73.9% when only face valid scales were used to produce
classification results. Incremental accuracy attributable to inclusion of the subtle scales was
shown to be 15%.
Accuracy figures range from 98.2% to 85.3% with significant difference in accuracy across
treatment settings, phi+. 15, p < .001. The data suggest that the relatively high level of accuracy
in the general psychiatric sample reflect the ability of the SASSI-3 to detect substance use
disorders within a population of individuals who have other psychiatric disorders as well. Thus,
this provides evidence that the SASSI-3 is specifically identifying substance use disorders rather
than a broader range of general maladjustment. By contrast, the SASSI-3 was less accurate in
identifying substance abusers in addictions treatment settings. The overall SASSI-3 accuracy rate
of identifying substance abusers (87.4%) is lower than that of identifying those who are
substance dependent (96%).
In summary, cross validation of the SASSI-3 scoring system yielded sensitivity of 93.2%,
specificity of 95.2%, positive predictive power of 98.7% and negative predictive power of
77.7%. An examination of the entire validity sample combined (n=839) reveals sensitivity of
93.9%, specificity of 94.2%, and positive predictive power of 98.4% and negative predictive
power of 79.8%. The data were also examined to determine the effects of treatment setting,
general adjustment, education, employment status, ethnic group membership, gender, age, and
marital status on the accuracy of SASSI-3 decision rule results. Findings indicated no significant
effects of any of these variables on the accuracy of SASSI-3 classifications with the exception of
treatment setting.
APP Scale and Psychometric Properties of the APP
The APP, a forced choice (yes/no), 21 item scale, was developed using data identifying
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personality items from a battery of personality tests that were linked to both a family history of
alcoholism and a current diagnosis of alcoholism in a cross-sectional general population sample
(Barnes et al., 2000). The APP is a relatively new instrument with limited research available
regarding psychometric properties. However, the available data, to date, appear to favorably
support the validity and reliability of the measure.
Anderson, et al., (2004) examined psychometric properties and the long- term predictive
validity of the APP scale in a general population sample in seven year longitudinal follow-up
study to the original WHDS ( Barnes et al., 2000). Internal consistency and test-retest reliability
coefficients were calculated. Three popular personality instruments, the Revised Eysenck
Personality Questionnaire (EPQ-R; Eysenck et al., 1985), the NEO Five Factor Inventory Form S
(FFI; Costa and McCrae, 1992) and the short form of the Temperament and Character Inventory
(TCI; Cloninger et al., 1994), all measured at follow-up, were used to help assess concurrent
construct validity. The MAC (MacAndrew, 1965) and the EPQ-A (Gossop and Eysenck, 1980)
scales were included for comparison purposes. Results indicate internal consistency (alpha= .74;
n= 788) versus MAC (alpha= .62) and EAP-A (alpha = .64). Therefore, the APP was found to be
superior in terms of internal consistency to the MAC, and EPQ-A. In terms of predictive validity,
the APP was found to be a significant predictor of the development of new alcoholic cases over a
7-year follow-up study after controlling for confounding variables supporting the predictive
validity of the APP (Anderson et al., 2004). Also, the pattern of cross-sectional correlations
between the APP and the three popular personality instruments EPQ-R, FFI and TCI (Anderson,
et al., 2004) provides support for the construct validity of the APP The APP was the most
significant predictor of SUD’s, suggesting that a specialized measure is inherently superior to
more general personality systems if the goal is to measure addiction-proneness traits using a
single scale. The APP examines personality traits that are premorbid to alcoholism versus traits
that have been accentuated by a history of abuse as with the other measures. The authors
conclude that the APP test appears to be a reliable, both in terms of internal consistency and
stability over time. Therefore, due to the reliability, validity, brevity and cost effectiveness, the
measure could likely be easily incorporated into both studies of substance use patterns over time
and substance abusers in treatment settings.
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Analysis of the Data:
All data were analyzed using PASW Statistics version 18.0 using an alpha level of .05. The
study was a construct validity study of the APP with five sub-validity components of internal
consistency, factor validity, convergent validity, incremental validity and criterion validity.
Research Question #1. What is the internal consistency of the APP scale?
In order to determine internal consistency for the APP, Cronbach alpha reliability coefficients for
the clinical, student and combined groups were derived through the SPSS reliabilities program.
The alpha coefficient assumes unidimensionality of the scale. A score above .80 indicates that a
given score on the APP can be used with individuals. Sample sizes of Clinical (N=101) and
Students (N=98) are sufficient to derive alpha coefficients for each group.
Research Question #2. What is the factor validity of the APP scale?
To determine Factor Validity of the APP, an exploratory factor analysis using orthogonal
rotation was conducted to derive the internal structure of the APP. This assumes that factors
derived are uncorrelated. The eigenvalue >1.0 rule applied for the identification of factors. The
APP was found to be multidimensional and the individual factors were interpreted by the
investigator. A combined sample size of 199 is sufficient to conduct an exploratory factor
analysis of the APP with 21 items. The ratio of items to participants is 1:9.
Research Question #3. What is the convergent validity of the APP scale using the SASSI-3
Screening Inventory?
Convergent validity was investigated first by calculating Pearson Product Moment correlations
between APP scale and the individual scales of the SASS-3. Correlations greater than .50
indicate moderate convergence (i.e. share 25% common variance). This criterion was set by the
researcher. Using G* Power, minimum sample size for each correlation is 64 with alpha at .05
and beta .95.
Research Question #4. What is the incremental validity of the APP scale?
In order to determine the incremental validity of the APP scale to the SASSI-3 scale total score, a
hierarchical regression analysis was performed using the best SASSI-3 predictor scales derived
through a stepwise regression and then adding the APP scale with SASSI-3 total score as the
criterion variable. This procedure establishes the extent to which the APP scale contributes
unique variation to the prediction of the SASSI-3 total score i.e. probability of substance
dependency. Using G* Power, minimum sample size is 138 with five predictors, alpha at .05 and
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beta .95.
Research Question #5. What is the criterion validity of the APP scale using two criterion
groups: clinical subjects and college students?
An ANOVA was conducted to determine whether there are significant differences between two
criterion groups: clinical and students. Part A investigated the criterion validity of the APP scale
or its ability to distinguish between the two groups. Part B investigated the SASSI-3 scales and
their ability to distinguish between the two groups. Using G*Power, minimum sample size is 210
with 2 groups, alpha at .05 and beta .95. If beta is .80, minimum sample size is 128.
With 199 participants, there is sufficient power to conduct all analysis (Costello & Osborne,
2005).
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CHAPTER 4
RESULTS
This chapter presents the statistical results of the study and addresses the five research
questions, and their related hypotheses. The five research questions relate to the internal
consistency of the APP, the factor validity analysis, convergent validity of the APP, the
incremental validity of the APP as a predictor of addiction proneness, and the criterion validity
of this instrument.
Research Question #1: What is the internal consistency of the APP scale?
In order to determine the internal consistency, Cronbach alpha reliability coefficients for the
clinical, student and combined groups were derived. The alpha coefficient assumes
unidimensionality of the scale. A score above .80 indicates that a given score on the APP can be
used with individuals.
Hypothesis:
The alpha coefficient will be .80 or above, indicating that the APP has homogeneous items with
heterogeneous populations and has strong internal consistency.
The Cronbach Alpha Reliability coefficients are summarized in Table 2 below.
TABLE 2: Internal Consistency of the APP Scale Population Mean Standard
Deviation Cronbach’s Alpha
Clinical
32.84
4.55
.800
Student
33.23
3.23
.591
Combined
33.04
3.92
.719
*APP total score coded as 1= yes response to items; 2= no response to items. Therefore, total score ranges from 21-42. Table 2 displays alpha reliability coefficients and indicates an alpha of .800 in the clinical group,
an alpha of .591 in the student group and a combined alpha of .719. Therefore, the hypothesis is
accepted for the clinical group but rejected for the student group and the combined group.
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Table 3 below summarizes item total correlations which measure the extent to which each item
on the APP scale relates to the total scale score. The optimal range is .25 to .75.
TABLE 3: Item Total Correlations of the APP for the Combined Groups
APP Item
Corrected
Item Total
Correlation
1. Have you had very strange or peculiar experiences? .327
2. Have you often gone against your parent’s wishes? .392
3. Are you a steady person?* .355
4. Do you wish you could have more respect for yourself? .306
5. Have you ever been in trouble with the law? .062
6. Do you prefer rock music over ballads? .052
7. Have your parents often objected to the type of people you went around with? .483
8. Have you lived the right kind of life?* .303
9. Have people said that you sometimes act too rashly? .425
10. Do you prefer loud music over quiet music? .161
11. Are you unable to keep your mind on one thing? .344
12. Do you go to church almost every week?* .170
13. Do you prefer sports cars over passenger cars? .104
14. Do you often feel “fed up”? .492
15. Do you have strange or peculiar thoughts? .446
16. Would you prefer to be a stunt man/woman over prop man/woman? .322
17. Do you prefer endurance sports over games with rests? .193
18. Did you ever feel that strangers were looking at you critically? .345
19. Did you play hooky from school quite often as a youngster? .413
20. Do you prefer electric music over un-amplified music? .061
21. Do you give money to charities?* .213
Note: All items have yes/no responses. For items marked by an asterisk, a negative response adds one point to the APP scale score. For all other items an affirmative response adds one point to the APP scale score. *Items are reverse scored. **1= yes response to items; 2= no response to items ***Bolded numbers denotes significance (between .25 and .75) Table 3 indicates that eight of the items fell out of the optimal range of .25- .75 (#5, #6, #10, #12,
#13, #17, #20, #21). This indicates that these items contribute little, if any, to the measurement of
the construct of addiction proneness.
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Research Question #2: What is the factor validity of the APP?
An Exploratory Factor Analysis (EFA) of the APP was conducted using orthogonal rotation to
derive the internal structure of the scale. This assumes that factors derived are uncorrelated. The
eigenvalue >1.0 rule was applied for the identification of factors. This analysis will determine
whether the APP is unidimensional or multidimensional in structure.
Hypothesis: The APP has a single underlying dimension, with an eigenvalue greater than 1.0.
Eigenvalues of the extracted components of the APP scale are presented in Table 4 below.
TABLE 4: Initial Eigenvalues based on Principal Components Analysis of the Combined
Groups Component
Total Eigenvalue
% of Variance
Cumulative Variance
1
3.847
18.321
18.321
2
2.102
10.008
28.328
3
1.472
7.010
35.339
4
1.382
6.581
41.920
5
1.206
5.741
47.661
6
1.023
4.873
52.534
The results of the EFA indicate that 6 factors have eigenvalues of 1.0 or above. Thus, the APP is
a multidimensional scale. The hypothesis is rejected.
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Figure 1 illustrates a scree plot of the eignevalues and APP items with the varimax rotation.
Figure 1 shows that three factors emerged as interpretable. The scree plot shows that the first
three factors fall above the Scree. The three factors were interpreted as 1) Negative Views, 2)
Impulsivity/Recklessness and 3) Sensation Seeking.
Table 5 presents the results of the varimax rotated component matrix.
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TABLE 5: Rotated Components Analysis Using Varimax Rotation for Combined Groups
*Bolded numbers denotes significance > .250
Table 5 displays the loadings of APP items on the three interpretable factors. There were nine
items loading on Factor 1 (NV). These items appear to be related to the general construct of
Negative Views (peculiar experiences .749; gone against parent’s wishes .431, ever been in
Component
APP Item
1
Negative Views
(NV)
2
Impulsivity/Recklessness
(IMP/R)
3
Sensation Seeking
(SS)
1. Have you had very strange or peculiar experiences? .749
-.105
-.081
2. Have you often gone against your parent’s wishes? .431
.009
-.044
3. Are you a steady person?* -.295
-.484
-.269
4. Do you wish you could have more respect for yourself?
.362
.589
-.319
5. Have you ever been in trouble with the law? -.261
.036
.074
6. Do you prefer rock music over ballads? -.159
-.054
.580
7. Have your parents often objected to the type of people you went around with?
.319
.247
.029
8. Have you lived the right kind of life?* -.049
-.722
-.001
9. Have people said that you sometimes act too rashly? .459
.198
-.225
10. Do you prefer loud music over quiet music? .014
.034
.729
11. Are you unable to keep your mind on one thing? .439
.208
.154
12. Do you go to church almost every week?* -.052
.002
-.176
13. Do you prefer sports cars over passenger cars? -.033
-.082
.159
14. Do you often feel “fed up”? .624
.374
-.005
15. Do you have strange or peculiar thoughts? .734
.083
-.031
16. Would you prefer to be a stunt man/woman over prop man/woman?
.250
-.084
.229
17. Do you prefer endurance sports over games with rests?
-.021
.176
.094
18. Did you ever feel that strangers were looking at you critically?
.583
.262
.005
19. Did you play hooky from school quite often as a youngster?
.080
.651
.000
20. Do you prefer electric music over un-amplified music?
.028
.018
.707
21. Do you give money to charities?* -.007
-.119
.047
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trouble with the law -.261, parents objected to people went around with .319, act rashly .459,
unable to keep mind on one thing .439, fed up .624; peculiar thoughts .734; and strangers looking
at you critically .583). There were four items loading on Factor 2 (IMP/R).These items seem to
be related to the construct of impulsivity/recklessness or lack of conformity ( i.e. steady person, -
.484; wish more respect for self, .589; living the right kind of life, -.722, and playing hooky from
school, .651). There were three items loading on Factor 3 (SS).These items appear to be
associated with the construct of Sensation Seeking (preferring rock music over ballads, .580;
prefer loud music over quiet, .729; prefer electric music over un-amplified, .707).
The hypothesis that the APP is a unidimensional scale is rejected. The orthogonal rotation
maximizes the differences amongst factors thus further separating the factors. The conclusion is
that while there were six factors with eigenvalues greater than 1.0, results of the scree plot
indicate that there are three interpretable factors named Negative Views,
Impulsivity/Recklessness and Sensation Seeking.
Research Question #3: What is the convergent validity of the APP using the SASSI-3?
Convergent validity was determined by deriving Pearson Product Moment Correlations between
the APP and SASSI-3 scales. Correlations greater than .50 indicate moderate convergence,
which means they share 25% common variance.
Hypothesis: The APP total score will be significantly (p<.05, r >.138) related to the SASSI-3
subscale and total scores.
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Correlations between the APP scale total score and SASSI-3 scales and total score are presented in Table 6. TABLE 6: Correlations between APP Total and SASSI-3 Scales and SASSI-3- Total Score
for Combined Groups
** p<.01
*** p<.001
APP Scale scores range from 0-21 where items were scored 0=no and 1= yes.
SASSI-3 Total scale was scored 1=not substance dependent, 2= maybe (high defensiveness
score indicates other scale scores are artificially lower) and 3= yes substance dependent.
Table 6 shows the correlations between the APP total score and SASSI-3 scales and Total score
i.e. the probability of having substance dependence. The SASSI-3 scales of Face Valid Alcohol
and Face Valid Other Drugs have higher standard deviations than their means indicating a highly
Scale
SASSI-3 Mean SD
APP Mean SD
r ES
FVA Face valid Alcohol
5.49
6.06
8.83
3.53
.527***
.28
FVOD Face valid other drugs
4.48
8.34
8.83
3.53
.507***
.26
SYM Symptoms
3.24
2.43
8.83
3.53
.526***
.28
OAT Obvious attributes
4.31
2.12
8.83
3.53
.368**
.14
SAT Subtle
attributes
2.79
1.20
8.83
3.53
.291**
.08
DEF Defensiveness
5.33
2.22
8.83
3.53
-.046
.02
SAM Supplemental
addiction measure
6.09
2.10
8.83
3.53
.520***
.27
FAM Family
8.57
1.85
8.83
3.53
-.305**
.09
COR Correctional
4.96
2.66
8.83
3.53
.467**
.22
SASSI-3 Total
1.70
.875
8.83
3.53
.379**
.14
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skewed distribution. Therefore, the results of these scales should be interpreted with caution due
to the violation of the assumption of normal distribution.
The correlations of the APP Total score and the SASSI-3 scales are all significant at p<.001 level
since they are above . 05 except for the defensiveness (DEF) scale. . However, only four (FVA,
FVOD, SYM, and SAM) of the nine scales have a moderate relationship >.50. The APP scale
does appear to have moderate convergent validity with four of the nine SASSI-3 scales but not
with the total SASSI-3 score(r =.379). Therefore, the hypothesis is accepted for eight of the nine
SASSI-3 subscales and for the Total. Only the correlation between the APP and the SASSI-3
Defensiveness scale failed to reach significance. The conclusion is that since the APP possesses
moderate convergence for four of the nine scales and weak convergence for 4 of the scales and
the total score, and zero for one scale (DEF), the two measures may be considered related but
independent.
Research Question #4: What is the incremental validity of the APP?
A hierarchical regression analysis using the SASSI-3 predictor scales and APP Total scores was
employed to predict substance dependence.
Hypothesis:
The APP captures additional variation in the prediction of probability of substance dependence.
Table 7 summarizes the results of a Hierarchical Regressions Analysis using the SASSI-3
predictor scales and then adding the APP Total score to test incremental validity of the APP as a
predictor of substance dependence.
TABLE 7: Model Summary with SASSI-3 scores entered first followed by the APP as
predictors of probability of substance dependency (n=199) Model R
R sq. Adj. r sq. R sq. change F change P
1 .668a
.446 .434 .446 39.01 .001
2 .675b
.456 .442 .010 3.603 .059
a. Predictors: FVA, FVOD, SYM, SAM derived through preliminary stepwise regression
b. Predictors: FVA, FVOD, SYM, SAM, APP Total
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Table 7 shows that the APP scores did not capture significant (p <.05) incremental variation in
predicting the SASSI-3 total score. It appears that the two measures are related constructs but
that the APP does not add any significant or unique value to predicting substance dependence.
The hypothesis that the APP will capture additional variation and add value in predicting
substance dependence is rejected. The APP adds little in the way of incremental validity when
used with the SASSI-3 to predict substance dependence with these two combined populations.
Research Question # 5, Part A: What is the criterion validity of the APP using two
populations: Clinical subjects and College Students?
An ANOVA was conducted to ascertain whether there are significant differences between the
two criterion groups with respect to a) addiction proneness and b) substance dependence. Part A
investigated the criterion validity of the APP or its ability to distinguish between the two groups
and part B investigated the SASSI-3 scales and their ability to distinguish between the two
groups.
Hypothesis #5, Part A: The APP will significantly discriminate between the clinical and student populations. Results of the criterion validity of the APP scale in distinguishing between the two groups are
presented in Table 8.
Table 8: ANOVA Comparing Clinical and Student Populations using the APP as the
Dependent Variable Group
N Mean SD F P d
Clinical
101
9.00
3.826
.450
.503
.097
Student
98
8.66
3.217
*p < .05
85
Table 8 shows that the APP did not significantly (p <.05) discriminate between the clinical and
student populations. The hypothesis that the APP will discriminate between the two groups was
rejected. The conclusion is that clinical and student groups are not significantly different with
respect to addiction -proneness and, therefore, fails to demonstrate criterion validity for the APP
with these groups.
Research Question # 5, Part B: What is the criterion validity of the SASSI-3 using two
populations: clinical subjects and college students?
Hypothesis #5, Part B:
The SASSI-3 scales and SASSI-3 total score will significantly differentiate between the clinical
and student populations.
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Table 9 shows results of the criterion validity study employing the SASSI-3 using clinical and
student populations.
Table 9: Univariate Tests of SASSI-3 Scales to Differentiate Clinical and Student
Populations SASSI-3 scales/group Means SD F P d
FVA face valid alcohol clinical student
6.65 4.29
6.995 4.655
7.853
.006
.405
FVOD face valid other drug clinical student
6.34 2.56
10.083 5.472
10.685
.001
.486
SYM symptoms clinical Student
3.59 2.87
2.585 2.204
4.541
.034
.301
OAT obvious attributes clinical student
4.67 3.93
2.006 2.174
6.313
.013
.354
SAT subtle attributes clinical student
3.06 2.51
1.231 1.105
10.941
.001
.471
DEF defensiveness clinical student
5.96 4.68
2.486 1.697
17.805
.000
.612
SAM supplemental addiction measure clinical student
6.86 5.30
1.929 1.986
31.827
.000
.797
FAM family clinical student
8.51 8.62
1.792 1.908
.168
.682
-.059
COR correctional clinical student
5.46 4.46
2.770 2.446
7.218
.008
.383
SASSI-Total clinical student
.195 .145
.876 .801
17.731
.000
.596
*SASSI-Total scored as 1=not substance dependent; 2= maybe, due to high defensiveness
score; 3= yes substance dependent.
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Table 9 indicates that nine of the 10 SASSI-3 scales differentiate these two groups. Only the
family scale (FAM) was not related to group membership. Therefore, the hypothesis is accepted
and the SASSI-3 possesses criterion validity. In all scales in which there were significant
(p<.05) differences between groups, the clinical group earned higher scores than the student
group with respect to the 9 dimensions of substance dependence. Moreover, the effect sizes of
these nine scales were >.25 indicating moderate to strong (d >.50) effect. Defensiveness (DEF)
and supplemental addiction measure (SAM) were the strongest discriminators of the two groups.
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CHAPTER 5
DISCUSSION
The purpose of the study was to investigate the validity of the APP Scale in terms of internal
consistency, factor validity, convergent validity, incremental validity, discriminant validity and
criterion validity. This instrument represents a unique scale developed for the purpose of
identifying individuals at risk or vulnerable to developing substance use disorders given specific
personality traits. Previous measures have focused on screening and assessing individuals who
have established substance use disorders. The utility of a measure of premorbid vulnerability is
in providing intervention and prevention. The study provided mixed results in terms of the utility
of the APP scale in research and clinical practice. The results are discussed as they apply to the
two populations sampled, college students and clinical subjects adjudicated for treatment.
Summary and Conclusions:
Research study #1 investigated the internal consistency of the APP Scale. It found a strong
alpha value of .80 for the clinical sample. However, the student and combined sample alphas
were not sufficiently strong to justify its use in either clinical or research contexts. These results
could be due to the different response patterns of the two groups and/or the forced-choice
structure of the scale. The alpha coefficients in this study are consistent with the findings in
previous research (Barnes et al, 2000; Anderson, 2003; Anderson et al., 2006) using general
populations and clinical populations.
Research study #2 investigated the factor validity of the APP scale in order to examine the
internal structure of the instrument. This study was aimed at establishing whether or not the APP
scale has a single underlying dimension or multiple dimensions. Three subscales emerged,
through an exploratory factor analysis (EFA), and these factors were consistent with those found
by Anderson (2003). The factors identified were Negative Views (NV),
Impulsivity/Recklessness (IMP/R) and Sensation Seeking (SS). These results are consistent with
the previous literature which identified personality traits referred to as psychoticism and
neuroticism, externalizing versus internalizing, and behavioral disinhibition (Babor et al., 1992;
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Blackson et al., 1994; Zucker et al., 1996; Anderson, 2003). This finding suggests that the
construct of addiction proneness may be too complex to be measured in terms of a single
construct. This provides further evidence of the limited reliability of the measure given that the
item pool is not homogeneous. Consequently, the APP in its present form should not be used as a
screening instrument to identify addiction proneness in clinical settings.
Study #3 examined the convergent validity of the APP with the SASSI-3 and found a
moderate but not strong relationship between the two measures. While they share some common
variance, the small effect sizes indicate limited overlap between the measures while the results
suggest that the two measures appear to be indicators of similar constructs, i.e. addiction
proneness (APP) and probability for substance dependence (SASSI-3). The results also indicate
that the two constructs differ. This is an important finding since attempting to measure addiction
proneness would be expected to predict vulnerability for the development of substance
dependence. Finally, the APP was found to be significantly correlated with all SASSI-3
subscales with the exception of the defensiveness scale (DEF). This scale was developed to
capture respondents’ level of guardedness or unawareness of substance abuse problems.
Unusually high or low scores on this scale reflect attempts to fake good or fake bad. Evidence
suggests that the combined group answered the SASSI-3 items with limited guardedness.
In study #4, the incremental validity of the APP scale was investigated using the SASSI-3 total
score and four predictor scales. These scales were identified through a stepwise regression
analysis to determine whether the APP score would yield significant incremental value in
predicting substance dependence. Findings indicated that there was no significant value in using
the APP to predict substance dependence and that its use as a predictor or screener with these
populations is limited. As a measure meant to screen for personality traits predicting
development of substance use disorders, the APP, at least in its present form, appears to have
very limited utility in that it is unable to increase the prediction of this problem behavior. Also,
since the SASSI-3 has four scales out of nine which appear to best predict probability of
substance dependence, perhaps an abbreviated measure of the SASSI-3 could be used as a
screener. These results may in part be due to the shared variance between the two measures, but
there appears to be no increased variation and therefore, no incremental validity with these two
populations.
90
Study #5 investigated the criterion validity of the two measures and found that the SASSI-3
score was able to distinguish between the clinical and student groups while the APP score was
not. Thus, the APP has limited discriminant validity with these two populations. This finding is
unexpected since the clinical group is already experiencing consequences of a substance use
disorder in that these participants are in treatment. This may be further evidence that addiction
proneness and probability of substance dependence are different constructs.
In summary, the findings of the five validity studies conducted in this research project suggest
that the APP lacks internal consistency with a non-clinical student population. There appear to be
three subscales measuring additive prone personality traits reflected in the previous literature.
These traits were Negative Views or Neuroticism, Impulsivity/Recklessness, and Sensation
Seeking or Psychoticism. However, there does not appear to be a single, well-defined underlying
dimension. Thus, the overall or total score may not be useful for predicting addiction proneness
in non-clinical populations. In addition, while the APP has moderate convergent validity with the
SASSI-3, the two appear to be measuring different constructs. Also, the APP has no incremental
validity in that it adds no unique information in measuring probability of substance dependence.
The criterion validity of the APP is limited since the measure was unable to distinguish between
a clinical population and a student population. It was expected that the measure would
discriminate between various populations since it was developed as a screening or predictive
measure of vulnerability to developing substance use disorders.
Limitations of the Study
In this section, threats to internal and external validity of the study will be discussed.
In terms of internal validity, there were issues of possible experimenter bias since the present
investigator administered the measures to both groups, and also scored and interpreted the data.
This procedure was due to restrictions of time and practicality of completing the research.
Therefore, since there was no independent corroboration of the testing procedures for either
group, the possibility of experimenter bias exists.
Also, the two groups completed the instruments in different settings and formats. The college
students were administered the measures in a group format, in classroom settings while the
clinical group data were collected individually during the initial intake in an outpatient office
91
setting. This could have impacted the way in which subjects responded to the measures. The
college student group’s data were used strictly for research purposes while the clinical subjects
were aware that the results of their inventories would also be used as part of determining the
treatment plan, as well as length and cost of treatment. Clinical subjects are mandated by the
Department of Motor Vehicles to complete treatment in order to reinstate their driving privileges.
Clients referred for evaluation and treatment for substance use disorders under these
circumstances are more likely to exhibit higher rates of defensiveness (guardedness) and lower
motivation to participate openly in treatment. Thus, it is likely that the two groups would have
different motivation for some of their responses. In the present study, the clinical population was
less forthright in their responses as indicated by the SASSI defensiveness scale. In fact, clients
referred for treatment due to having a DUI charge are often hostile to the notion that they need
treatment, especially during the initial evaluation session.
In terms of threats to external validity, the two sample groups may not be representative of
the larger population due to several factors. Both groups likely have biases due to the regional
setting (Southern U.S). The college students were recruited from higher level, junior and senior
undergraduate courses in the department of Educational Psychology at Florida State University.
The clinical subjects were recruited from a population of DUI offenders referred for treatment in
Jacksonville Florida to a private, outpatient clinician's office. It is worth noting here that the
clinical group may represent a less extreme clinical population since they are outpatients in a
psycho-educational treatment, referred due to driver risk indicators including DUIs as opposed to
participants in a more intensive level of treatment. Also, the results of data collected from
students in upper level classes and those choosing elective courses in education and human
relation classes may not be representative of the general population. With the clinical group,
those who choose to be evaluated and treated in a private practice setting versus group setting
offered at a community service agency likely have different demographics. For example,
differences in SES, ethnic background, and educational level may exist. Consequently,
participants in this study may not represent the larger population of DUI offenders referred for
treatment. Due to regional or geographical based cultural biases, the data collected may not be
generalizable to larger general populations.
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Implications for Practical Use
The results of this study suggest that the APP, in it’s present form, would not be recommended
for use in applied, clinical contexts. This conclusion is based on weak internal consistency,
absence of a uniform underlying factor of addiction proneness, the lack of incremental validity
using the established SASSI-3 with the groups studied, and overall weak construct validity. The
APP was unable to discriminate between the two groups while the SASSI-3 did discriminate
between the groups. Therefore, the APP seems to add no additional information to a well
established instrument and could not be used in place of the SASSI-3. In addition, in it’s present
form, it would not be useful as an adjunct to the SASSI-3. The APP was developed to measure
addiction-proneness and it is unclear from this study whether this construct is well defined and
accessible through the use of the APP. The SASSI-3, which measures probability of having a
substance use disorder, and the APP, meant to measure addiction-proneness, are measuring
similar yet different constructs. There is some evidence that the APP may have sufficient
reliability with clinical groups such as the one used in this study. However, the low reliability
with the college student group would tend to negate the purpose of the measure which was to
provide early detection of those at risk for developing substance use disorders.
Implications for Future Research and Recommendations
Given the preceding discussion regarding the limitations of the APP scale due in part to
limited psychometric properties, it follows that more research is needed to improve the scales’
reliability and its construct validity. Specifically, the APP items do not appear to be measuring
an underlying construct of addiction proneness as expected. There appear to be three subscales
which do corroborate previous findings in that these subscales reflect the growing body of
evidence that addiction proneness is correlated with personality traits of psychoticism and
neuroticism. While these subscales could prove useful in predicting addiction proneness, the
overall score is not useful since it cannot be interpreted. The APP would need to be further
refined to enhance the psychometric properties of the instrument. This may be accomplished by
increasing the number of items, identifying items that more accurately relate to addiction
93
proneness, and changing the wording of items which may be vague or outdated in their language.
Another improvement may be to change the response format from a forced choice, true/false
scale to a Likert-type scale which would provide a range of options for the respondent. Overall,
the APP seems to be attempting to measure too complex a construct with too simplistic or
imprecise an instrument.
The phenomena of addiction proneness appears to be complex and multidimensional and more
research is warranted to accurately identify and define the construct. Barnes et al., (2000) efforts
to identify the construct through use of existing personality and substance use disorder measures
with clinical and general populations represents a major contribution to this area of study. The
instrument was first developed as a measure of pre-alcoholic vulnerability and further refined to
include other types of drugs and addiction. In clinical settings, it is widely acknowledged that
despite the drug of choice or addictive behavior of choice, the underlying personality dynamics
appear to be consistent. These dynamics involve primarily escapism (relief use) or sensation
seeking (seeking euphoria). Further, the notion that there appear to be two types or clusters of
addicts and two pathways to developing substance use disorders has been well established in the
literature.
Further, different population samples could be used with larger sample sizes to improve the
generalizability of the APP. For example, using college students from different regions and areas
of study, clinical samples in various types of treatment settings and modalities such as group
treatment versus individual treatment, community service samples versus private practice
samples, and more intensive versus less intensive levels of treatment. It may be beneficial to
sample a wider age range starting as young as age 12 since this may reveal tendencies toward
SUDs while in young adult samples, SUDs may already be present. In the current study, the
clinical and student population results overlapped. This overlap could indicate that both groups
have already experienced the occurrence of substance use disorders.
The refinement of a scale that helps identify what makes individuals vulnerable to developing
(SUDs) is a worthy endeavor and dictates further research. There is a need to better identify
individuals at risk and target prevention and intervention efforts given the tremendous impact
SUDs have on individuals, families and society. Once the construct of addiction proneness is
better understood and measured, it may be possible to target more specific prevention and
94
intervention strategies for specific types of addicts. For example, individuals exhibiting higher
rates of sensation seeking could be encouraged to learn coping skills and ways to redirect their
needs towards less self-destructive activities. Individuals with a propensity to be impulsive and
reckless could be taught through cognitive behavioral strategies to establish a “speed bump” or
connection between action and consequence. Individuals who exhibit tendencies towards
neuroticism or internalizing behaviors such as depression could be offered supportive therapy in
conjunction with psychotropic medication versus using drugs to self-medicate. In clinical
settings, a brief, precise measure which is easily administered scored and cost effective is
optimal. As behavioral scientists, psychologists who are involved in research could focus efforts
on identifying and measuring constructs which clinicians can then use to enhance their efforts at
intervention.
In summary, the present study sought to examine the construct validity of the APP scale with a
college student population and a clinical population with 5 sub-studies of validity. It was found
that the APP was not useful as a predictor of addition proneness. This instrument needs rather
extensive development work in order to identify the construct, addiction proneness, more clearly
and to measure the construct more precisely. Addiction proneness appears to be a complex
phenomena mediated by a latent predisposition and affected by insulating or aggravating
influences such as family history, biochemical and genetic factors, learning, cultural influences
and life experiences. The present study illuminates the need to first define a construct with
specificity prior to attempting to measure the construct in a meaningful or applicable way. The
research in this area does seem to support the prevailing biopsychosocial model of addictions and
personality, therefore highlighting the multifaceted nature of psychological issues versus
previous medical models. The greatest challenge in this area of research is in enhancing efforts to
better define addiction proneness and vulnerability to SUDs and how to capture this through
more comprehensive and precise measures. Further research with the APP aimed at refinement
of the items would likely enhance the APP’s psychometric properties and this would be a worthy
contribution to the area of measuring addiction proneness and thus have clinical applications for
prevention and intervention of developing addictions.
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APPENDIX A
Addiction-Prone Personality (APP) Scale Items
1. Have you had very strange or peculiar experiences?
2. Have you often gone against your parent's wishes?
3. Are you a steady person? *
4. Do you wish you could have more respect for yourself?
5. Have you ever been in trouble with the law?
6. Do you prefer rock music over ballads?
7. Have your parents often objected to the kind of people you went around with?
8. Have you lived the right kind of life? *
9. Have people said that you sometimes act too rashly?
10. Do you prefer loud music over quiet music?
11. Are you unable to keep your mind on one thing?
12. Do you go to church almost every week?*
13. Do you prefer sports cars over passenger cars?
14. Do you often feel "fed up?"
15. Do you have strange or peculiar thoughts?
16. Would you prefer to be a stunt-man/woman over a prop-man/woman?
17. Do you prefer endurance sports over games with rests?
18. Did you ever feel that strangers were looking at you critically?
19. Did you play hooky from school quite often as a youngster?
20. Do you prefer electric music over un-amplified music?
21. Do you give money to charities? *
Note: all items have yes/no responses; for items marked by an asterisk *, a negative response adds one point to the
APP scale score; for all other items, an affirmative response adds one point to the APP scale score
96
APPENDIX B
APP Norms
Norms from a general population sample (Winnipeg, Manitoba) are provided from Barnes et
al, (2000) and outlined below.
APP Norms for General Population (WHDS) Sample
APP Scale GENDER
GENDER Mean N Std. Deviation
0 female 5.4783 638 3.54839
1 male 7.0825 607 3.87346
Total 6.2604 1245 3.79471
APP Scale AGEGRP
AGEGRP Mean N Std. Deviation
18 to 34.9 years of age 8.5939 431 3.72712
35 to 49.9 years of age 5.7904 398 3.47182
50 to 67 years of age 4.2925 416 2.73700
Total 6.2604 1245 3.79471
APP Scale * DSMDIAG lifetime diagnosis of SUDs
DSMDIAG lifetime diagnosis Mean N Std. Deviation
0 no abuse or dependence 5.7341 1054 3.50660
1 abuse or dependence 9.1650 191 4.01444
Total 6.2604 1245 3.79471
97
APP scale norms in the general population sample indicate that male gender, age range 18-35
and a lifetime diagnosis of SUDs have higher overall scores on addiction proneness while in the
clinical sample, gender was not significantly different but overall scale scores were significantly
higher than in the general sample.
APP Norms for WHDS Clinical (AFM) Sample
APP Scale * GENDER
GENDER Mean N Std. Deviation
0 female 12.3125 112 4.41135
1 male 12.8239 284 4.52679
Total 12.6793 396 4.49480
APP Scale * DRUG OF CHOICE
DRUG OF CHOICE Mean N Std. Deviation
1. alcohol only 11.7917 240 4.35342
2. other drugs only 13.7600 50 4.42424
3. alcohol and other drugs 14.8987 79 3.89804
Total 12.7236 369 4.45686
Data from Anderson’s (2006) study with a clinical sample in treatment at University of New
Mexico indicate that drug of choice influences APP scores with stimulant users having higher
scores than alcohol only or other drug users.
APP Norms for University of New Mexico Clinical (Stimulants) Sample
APP Scale * STIM_TYP predominant stimulant type
STIM_TYP predominant stimulant type Mean N Std. Deviation
1 cocaine 13.2609 69 3.37662
2 methamphetamine with or without cocaine 14.5750 40 3.07085
Total 13.7431 109 3.31496
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APPENDIX C
SASSI-3
99
100
101
102
APPENDIX D
BIOPSYCHOSOCIAL MODEL OF SUBSTANCE USE DISORDERS
GLOSSARY OF TERMS:
Agreeableness: a tendency to be compassionate and cooperative with others.
Big Five Factor Model: McCrae & Costa’s model of personality comprised of dimensions of neuroticism (tendency to experience negative affect), Extroversion (gregariousness, excitement-
seeking), openness to experience (adventurousness, broad-mindedness), agreeableness
(helpfulness, compassion) and conscientiousness (dependability, responsibility).
Big Three Factor Model: Eysenck’s model of personality comprised of three broad dimensions of neuroticism (anxious, depressed, guilt feelings, tense, irrational, moody, shy tendencies)
minded tendencies) and Extroversion (sociable, lively, assertive, sensation-seeking, dominant
tendencies).
Binge drinking or heavy, episodic drinking: consumption of five or more drinks for men or four
or more drinks for women in about 2-hour period.
COA or children of alcoholics: individuals who have been raised in a family environment or by a
parent(s) who meet clinical criterion for alcohol or substance dependence and are at higher risk
for substance abuse/dependence and/or psychological issues as a result.
Conscientiousness: a tendency to show self-discipline, act dutifully, and aim for achievement.
Disease-like type: Jellinek’s typology of alcoholics who are viewed as physically dependent on alcohol. Gamma or binge drinkers are included in this type and are characterized by loss of
control once drinking starts but are able to abstain from alcohol between binges. Delta types, in
contrast, remain in control of drinking during the episode but are unable to abstain from
drinking.
Disinhibition: personality trait associated with substance abuse described as impulsive,
Essential type: Knight’s psychodynamic classification of types of alcoholics characterized by
individuals that are passive and fixated at the oral stage of psychosocial development.
103
Externalizing disorders: personality disorders that encompass disinhibitory personality traits
(impulsivity, novelty/sensation seeking and low conscientiousness/constraint) and antisocial
personality that act as risk factors/mediators/moderators and consequences of substance abuse.
Extroversion: a tendency towards gregariousness, excitement seeking, attention seeking.
Field Dependence: a tendency to rely on either internal or external referents in making
perceptual judgments.
Harm avoidance: a personality trait associated with substance abuse described as cautious,
apprehensive, inhibited and sensitive to punishment tendencies.
Impulsivity: a personality trait associated with substance abuse described as a tendency to
behave without forethought or considering the risk(s) involved in the behavior and a tendency to
exhibit sudden, unpredictable, spontaneous behaviors without due deliberation or regard for the
consequences and that occur under the influence of a compelling pressure that restricts the
individuals’ freedom of will.
Internalizing disorders: personality disorders that encompass negative emotionality or affective
traits or neuroticism, mood and anxiety disorders that act as risk factors/mediators/moderators
and consequences of substance abuse.
Introversion: a tendency towards shyness, social withdrawal and avoidance of excitement or
attention.
Negative Views: a tendency toward negative emotionality or pessimistic views;
superstitiousness, suspiciousness.
Neuroticism: a tendency to easily experience unpleasant emotions such as anxiety, anger, or
depression; emotional instability.
Non-disease type: Jellinek’s typology of alcoholics who are viewed as not physically dependent
on alcohol and include alpha and beta types.
Novelty seeking/sensation seeking/stimulus reducing: a personality trait associated with
substance abuse described as impulsive, excitable, exploratory, fickle, and disinhibited
tendencies and seeking of varied, novel, complex and intense sensations and experiences with
the willingness to take physical, social, legal and/or financial risk for the sake of such
experiences.
Openness to experience: appreciation for art, emotion, adventure, and unusual ideas; imaginative
and curious.
Psychoticism: a tendency to exhibit anti-social behaviors or lack of compassion, lowered social
consciousness, inhibitions and cooperativeness or antagonistic with others; a higher tendency
104
toward impulsivity, recklessness, irresponsibility, and a higher tendency towards sensation
seeking or novelty seeking.
Reactive type: Knight’s psychodynamic classification of types of alcoholics characterized by individuals that are more compulsive and reliable and fixated at the anal stage of psychosocial
development; later onset of use.
Reward dependence: a personality trait associated with substance abuse described as ambitious,
sympathetic, warm, industrious, persistent, moody and sentimental tendencies.
Type 1 or milieu/secondary/relief type alcoholic: Cloninger’s classification of a type of alcoholism characterized by later age of onset of use, experience guilt and fear in association
with drinking, loss of control, introversion, relief drinking, emotionally dependent,
perfectionistic, rigid, anxious and infrequently engaging in alcohol-related anti-social conduct;
higher neuroticism; more prominent in women.
Type 2 or male/primary type alcoholic: Cloninger’s second type of alcoholic characterized by earlier age of onset of use and alcohol related problems, less ability to abstain from alcohol use,
more frequent alcohol-related anti-social behavior, less loss of control once drinking commences
and less guilt or fear associated with drinking than type 1 alcoholics; higher Extroversion; more
prominent in men.
Type A alcoholic: Babor’s typology of alcoholism defined by later onset, less severe dependence, fewer alcohol-related problems, fewer childhood risk factors and less
psychopathology.
Type A substance abuser: Feingold’s typology extending Cloninger’s alcoholic types to other substance abusers; equivalent to Type 1 alcoholics with higher neuroticism and chronic stress.
Type B alcoholic: Babor’s typology of alcoholism defined by early onset, more severe dependence, more chronic treatment history, childhood risk factors, familial alcoholism,
polydrug use, greater psychopathological dysfunction and more life stress.
Type B substance abuser: Feingold’s typology of substance abusers, equivalent to type 2 alcoholics with background of antisocial personality traits such as poor socialization, sensation
seeking, impulsivity and aggression and history of delinquency and criminality; characterized by
earlier onset, more severe course of symptoms and positive family history.
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APPENDIX E
Office of the Vice President For Research
Human Subjects Committee
Tallahassee, Florida 32306-2742
(850) 644-8673 · FAX (850) 644-4392
RE-APPROVAL MEMORANDUM
Date: 3/18/2010
To: Sandi Sage
Dept.: EDUCATIONAL PSYCHOLOGY AND LEARNING SYSTEMS
From: Thomas L. Jacobson, Chair
Re: Re-approval of Use of Human subjects in Research
Validity of the Addiction-Prone Personality Questionnaire
Your request to continue the research project listed above involving human
subjects has been approved by the Human Subjects Committee. If your project
has not been completed by 3/16/2011, you are must request renewed approval by
the Committee.
If you submitted a proposed consent form with your renewal request, the
approved stamped consent form is attached to this re-approval notice. Only
the stamped version of the consent form may be used in recruiting of research
subjects. You are reminded that any change in protocol for this project must
be reviewed and approved by the Committee prior to implementation of the
proposed change in the protocol. A protocol change/amendment form is required
to be submitted for approval by the Committee. In addition, federal
regulations require that the Principal Investigator promptly report in
writing, any unanticipated problems or adverse events involving risks to
research subjects or others.
By copy of this memorandum, the Chair of your department and/or your major
professor are reminded of their responsibility for being informed concerning
research projects involving human subjects in their department. They are
advised to review the protocols as often as necessary to insure that the
project is being conducted in compliance with our institution and with DHHS
regulations.
Cc:
HSC No. 2010.4012
106
APPENDIX F
107
108
REFERENCES
Abbey, A. (2002). Alcohol-related sexual assault: A common problem among college
students. Journal of Studies on Alcohol, 14, 118-128. af Klinteberg, B., Andersson, T., Magnusson, D., & Stattin, H. (1993). Hyperactive behavior
in childhood as related to subsequent alcohol problems and violent offending: A longitudinal study of male subjects. Personality and Individual Differences, 15 (4), 381- 388.
American Academy of Pediatrics, Committee on Substance Abuse and Committee on
Children with Disabilities. (2000). Fetal alcohol syndrome and alcohol-related
neurodevelopmental disorders. Pediatrics,106, 358-361. American Psychiatric Association. (1952). Diagnostic and statistical manual of mental
disorders, (1st Ed.). Washington, DC: Author. American Psychiatric Association. (1967). Diagnostic and statistical manual of mental
disorders, (2nd Ed.). Washington, DC: Author. American Psychiatric Association. (1980). Diagnostic and statistical manual of mental
disorders, (3rd Ed.). Washington, DC: Author. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental
disorders, (3rd Ed.). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental
disorders, (4th Ed.). Washington, DC: Author. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental
disorders, (4th Ed.). Washington, DC: Author. Anderson, Robert Edmund. (2003). Investigating a quantitative measure of addiction-prone
personality. Doctoral Dissertation, University of New Mexico, Albuquerque, NM, p. 1-99.
as a prospective predictor in the development of alcoholism in a general population sample. Paper presented at the 9
th biennial meeting of the international society for
the study of individual differences, July 5-9, Vancouver, Canada.
109
Anderson, R.E., Barnes, G.E., Murray, R.P. & Miller, W.R. (2004). The dimensionality and
latent class structure of the Addiction Prone Personality (APP) measure. Alcoholism:
Clinical and Experimental Research, p. 67a. Anderson, R.E., Barnes, G.E., Patton, D. & Perkins, T.M. (1999). Personality in the
development of substance abuse. Psychology in Europe, Vol. 7, Tilburg University Press.
Anderson, Robert E., Clark, Vincent P., & Barnes, Gordon E. (2006). Test of a two-path
model of addiction-prone personality traits in a clinical sample. 26th
International
Congress of Applied Psychology, Athens, Greece. Anthony, J.C. (1992). Epidemiological research on cocaine use in the USA. In G.R. Bock &
J. Whelan (Eds.), Cocaine: Scientific and social dimensions, Chester, England: Wiley, 20-33.
Babor, T.F., De la Fuente, J.R., Saunders, J. & Grant, M. (1989). AUDIT, The Alcohol Use
Disorders Identification Test: guidelines for use in primary health care, World Health Organization, Geneva.
Babor, T. F., Hoffmann, M., DelBoca, F. K., Hesselbrock, V., Meyer, R. E., Dolinsky, Z. S.
& Rounsaville, B. (1992). Types of alcoholics, I: Evidence for an empirically derived typology based on indicators of vulnerability and severity. Archives of General
Psychiatry, 49, 599-608. Ball, S.A. (1995). The validity of an alternative five-factor measure of personality in cocaine
abusers. Psychological Assessment, 7, 148-154. Ball, S.A. (1996). The validity of an alternative five-factor measure of personality in cocaine
abusers. Psychological Association, 154. Ball, S.A. (1996). Type A and B alcoholism: Applicability across subpopulations and
treatment settings. Alcohol Health World, 20, 30-35. Ball, S. A. (2002b). Big five, alternative five and seven personality dimensions: Validity in
substance dependent patients, In: P. T. Costa Jr. and T.A. Widiger, (Eds.), Personality disorders and the five-factor model of personality (2
nd ed.), American
Psychological Association, Washington, DC. Ball, S. (2005). Personality traits, problems, and disorders: Clinical applications to substance
use disorders. Journal of Research in Personality, 39(1), 84-102. Ball, S.A., Carroll, K.M., Babor, T.P., & Rounsaville, B.J. (1995). Subtypes of cocaine
abusers: Support for a Type A distinction. Journal of Consulting and Clinical
Psychology, 63, 115-124.
110
Barnes, G.E. (1983). Clinical and prealcoholic personality characteristics. In B. Kissin & H. Begleiter (Eds), The pathogenesis of alcoholism: Psychosocial factors (pp. 113-195). Brooklyn: Plenum Publishing.
Barnes, G. E. (1985b). The Vando R-A scale as a measure of stimulus reducing-augmenting.
In J. Strelau, F. Farley & A. Gale (Eds.), The biological bases of personality and
behavior: Theories, measurement techniques and development (pp117-180). Washington, DC: Hemisphere.
Barnes, G.E., Barnes, M.D., & Patton, D. (2005). Prevalence and predictors of heavy marijuana use in a Canadian youth sample. Journal of Substance Use and Misuse, 40, 1849-1863. Barnes, G.E., Murray, R.P., Patton, D., Bentler, P.M., & Anderson, R.E. (2000). The
addiction-prone personality, New York, NY, US: Kluwer Academic/ Plenum Publishers.
Barratt, E.S., Stanford, M.S. & Patton, Jim H. (1995). Factor structure of the Barratt
Impulsiveness Scale. Journal of Clinical Psychology, 51(6), Nov 1995, 768-774. Bates, M. E., & Labouvie, E.W. (1995). Personality-environment constellations and alcohol
use: A process-oriented study of intra-individual change during adolescence. Psychology of Addictive Behaviors, 9(1), 23-35.
Battaglia, M., Przybeck, T. R., Bellodi, L., & Cloninger, C. R. (1996). Temperament
dimensions explain the comorbidity of psychiatric disorders. Comprehensive
Psychiatry, 37, 292-298. Bechara, A. & Damasio, H. (2002). Decision-making and addiction (part I): Impaired
activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia, 40(10), 1675-1689.
Bechara, A., Dolan, S., & Hindes, A. (2002). Decision-making and addiction (part II):
Myopia for the future or hypersensitivity to reward? Neuropsychologia, 40(10), 1690-1705.
Population and familial association between the D4 dopamine receptor gene and measures of sensation seeking. Nature Genetics, 12, 81-84.
Bickel, W.K., & Marsch, L.A. (2001). Toward a behavioral economic understanding of drug
dependence: Delay discounting process. Addiction, 96, 73-86. Blackson, T. C. (1994). Temperament: A salient correlate of risk factors for alcohol and drug
abuse. Drug and Alcohol Dependence, 36, 205-214.
111
Blaszczynski, A.P., Steel, Z.P., & McConaghy, N. (1997). Impulsivity in pathological gambling: The antisocial impulsivist. Addiction, 92, 75-87.
Blum, K., Noble, E.P., Sheridan, P.J., Montgomery, A., Ritchie, T., Jagadeeswaran, P.,
Nogami, H., Briggs, A.H., & Cohan, J.B. (1990). Allelic association of human dopamine D2 receptor gene in alcoholism. Journal of the American Medical
Association, 263(15), 2055-2060. Blum, K., & Payne, J.E. (1991). Alcohol and the addictive brain: New hope for alcoholics
from biogenic research. Toronto, Canada: Maxwell Macmillan. Booth, B.M. & Feng, W. (2002). The impact of drinking and drinking consequences on short-
term employment outcomes in at risk drinkers in six southern states. Journal of
Behavioral Health Services and Research, 29(2), 157-166. Boyd, G.M., & Faden, V. (2002). College drinking: what it is, and what to do about it: A
review of the state of science: Overview. Journal of Studies on Alcohol, 6-13. Bozarth, M.A. (1987). Ventral tegmental reward system. In J. Engel, L. Oreland, B. Pernov,
S. Rossner, & L.A. Pelhorn (Eds.), Brian reward systems and abuse (pp. 1-17). New York: Raven Press.
Brook, J.S., Brook, D.W., & Whiteman, M. (1999). Older sibling correlates of younger
sibling drug use in the context of parent-child relations. Genetic, Social, and General
Psychology Monographs, 125, 451-468. Brook, J.S., Whiteman, M., Gordon, A.S., & Brook, D.W. (1990). The role of older brothers
in younger brothers’ drug use viewed in the context of parent and peer influence. Journal of Genetic Psychology, 151, 59-75.
and substance use comorbidity among treatment-seeking opioid abusers, Archives of
General Psychiatry, 54, 71-79. Butcher, J.N., Dahlstrom, W.G., Graham, J.R., Tellegen, A. & Kaemmer, B. (1989).
Minnesota Multiphasic Personality Inventory-2 (MMPI-2): Manual for
administration and scoring. Minneapolis: University of Minnesota Press. Cacciola, J.S., Rutherford, M.J., Alterman, A.I., & Snider, E.C. (1994). An examination of
the diagnostic criteria for antisocial personality disorder in substance abusers. The
Journal of Nervous and Mental Disease, 182, 517-523.
Cadoret, R.J., O’Gorman, T.W., Troughton, E., & Haywood, E. (1985). Alcoholism and antisocial personality: Interrelationships, genetics and environmental factors. Archives of General Psychiatry, 42, 161-167.
112
Cadoret, R.J., Troughton, E., O’Gorman, T.W., & Haywood, E. (1986). An adoption study of genetic and environmental factors in drug abuse. Archives of General Psychiatry, 43, 1131-1136.
Cadoret, R. J., Yates, W. R., Troughton, E., Woodworth, G., & Stewart, M. A. (1995).
Genetic-environmental interaction in the genesis of aggressivity and conduct disorders. Archives of General Psychiatry, 52, 916-924.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the
multitrait-multimethod matrix. Psychological Bulletin, 56, 81-105. Carroll, K.M, Ball, S.A, & Rounsaville, B.J. (1993). A comparison of alternate systems for
diagnosing antisocial personality disorder in cocaine abusers. The Journal of
Nervous and Mental Disease, 181, 436-443. Carton, S., Jouvent, R., Bungener, C., & Widlocher, D. (1992). Sensation seeking and
depressive mood. Personality and Individual Differences, 7, 843-849. Caspi, A., Harrington, H., Moffitt, T. E., Begg, D., Dickson, N., Langley, J., & Silva, P. A.
(1997). Personality differences predict health-risk behaviors in young adulthood: Evidence from a longitudinal study. Journal of Personality and Social Psychology,
73(5), 1052-1063. Caspi, A., Moffitt, T.E., Newman, D.L., & Silva, P.A. (1996). Behavioral observations at age
3 years predict adult psychiatric disorders: Longitudinal evidence for a birth cohort. Archives of General Psychiatry, 53, 1033-1039.
Caspi, A., & Silva, P.A. (1995). Temperamental qualities at age three predict personality
traits in young adulthood: New evidence from a birth cohort. Child Development, 66, 486-498.
hypothesized to underlie the association between cluster B personality and substance use disorders. Journal of Personality Disorders, 16(5), 424-436.
Castaneda, R., Sussman, N., Westreich, L., Levy, R., O’Malley, M. (1996). A review of the
effects of moderate alcohol intake on the treatment of anxiety and mood disorders. Journal of Clinical Psychiatry, 57(5), 207-212.
Chassin, L., Flora, D.B., King, K.M. (2004). Trajectories of Alcohol and Drug Use and
Dependence From Adolescence to The Effects of Familial Alcoholism and Personality. Journal of Abnormal Psychology, 113(4), 483-498.
Clements, Richard. (2002). Psychometric properties of the Substance Abuse Subtle Screening
Inventory-3. Journal of Substance Abuse Treatment, 23(4), 419-423.
113
Cloninger, C. R. (1986). A unified biosocial theory of personality and its role in the development of anxiety states. Psychiatric Development, 4, 167-226.
Cloninger, C.R., (1987). A systematic method for clinical description and classification of
personality variants. Archives of General Psychiatry, 44, 573-588. Cloninger, C.R. (1987a). Neurogenetic adaptive mechanisms in alcoholism. Science, 236,
B.J., & Barrett, J.E. (Eds.). Psychopathology and the brain. New York: Raven Press, Ltd.
Cloninger, C.R. (1994). Temperament and personality. Current Opinions in Neurobiology, 4,
266-273. Cloninger, C. R., Bohman, M., & Sigvardsson, S. (1981). Inheritance of alcohol abuse: cross-
fostering analysis of adopted men. Archives of General Psychiatry, 38, 861-868. Cloninger, C.R., & Gottesman, I.I. (1987). Genetic and environmental factors in antisocial
behavior. In S.A. Mednick, T.E. Moffitt, & S.A. Stack (Eds.), The causes of crime:
New biological approaches (pp. 92-109). Cambridge, MA: Cambridge University Press.
Cloninger, C. R., Przybeck, T. R., Svrakic, D. M., & Wetzel. R. D. (1994). The temperament
and character inventory (TCI): A guide to its development and use. St Louis, MO: Center for Psychobiology of Personality, Washington University.
Cloninger, C.R., Reich, T. Sigvardsson, S., von Knorring, A.L., & Bohman, M. (1988).
Effects of changes in alcohol abuse. In R.M. Rose & J.E. Barrett (Eds.), Alcoholism,
origins, and outcomes (pp. 49-74). New York: Raven Press. Cloninger, C. R., Sigvardsson, S., & Bohman, M. (1988). Childhood personality predicts
alcohol abuse in young adults. Alcoholism: Clinical and Experimental Research, 12, 494-505.
Cloninger, C. R., Sigvardsson, S., Przybeck, T. R. & Svrakic, D. M. (1995). European
Archives of Psychiatry and Clinical Neuroscience, 245, 239-244.
114
Cloninger, C.R, Svrakic, D.M., & Przybeck, T.R. (1993). A psychological model of temperament and character. Archives of General Psychology, 50, 975-990.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues
for field settings. Chicago: Rand McNally. Comings, D.E., Rosenthal, R.J., Lesieur, H.R., Rugle, L.J., Muhleman, D., Chiu, C., Dietz,
G., & Gade, R. (1996). A study of the D2 receptor gene in pathological gambling. Pharmocogenetics, 6, 223-234.
Conrad, P.J., Pihl, R.O., Stewart, S.H., & Dongier, M. (2000). Validation of a system of
classifying female substance abusers on the basis of personality and motivational risk factors for substance abuse. Psychology of Addictive Behaviors, 14, 243-256.
Corrao, G. Bagnardi V., Zambon, A., & La Vecchia, C. (2004). A meta-analysis of alcohol
consumption and the risk of 15 diseases. Preventive Medicine, 38, 613-619. Corrao, G., Rubbati, L., Zambon, A., & Arico, S. (2002). Alcohol-attributable and alcohol-
preventable mortality in Italy. European Journal of Public Health, 12, 214-223. Costa, P. T., Jr., & McCrea, R. R. (1992). Normal personality assessment in clinical practice:
The NEO Personality Inventory. Psychological Assessment, 4, 5-13. Costa, P. T. & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO PI-R) and
Costa, P. T. Jr., & McCrae, R. R. (1995). Primary traits of Eysenck’s P-E-N system: Three-
and five-factor solutions. Journal of Personality and Social Psychology, 69, 308-317. Cottler, L. B., Price, R. K., Compton, W.M., & Mager, D.E. (1995). Subtypes of adult
antisocial behavior among drug abusers. The Journal of Nervous and Mental
Disease, 183, 154-161. Cronbach, L. J. (1971). Test validation. In R. L. Thorndike (Ed.), Educational measurement
(2nd ed., pp. 443-507). Washington, DC: American Council on Education. Darkes, J., Greenbaum, P.E., & Goldman, M.S. (1998). Sensation Seeking-Disinhibition and
Alcohol Use Exploring Issues of Criterion Contamination. Psychological
Assessment, 10(1), 71-76. Donovan, C., & McEwan, R. (1995). A review of the literature examining the relationship
between alcohol use and HIV-related sexual risk-taking in young people. Addiction, 90, 391-328.
115
Dragutinovich, S. (1986). Stimulus intensity reducers: Are they sensation seekers, extraverts or strong nervous types? Personality and Individual Differences, 8(5), 693-704.
Dush, D.M., & Keen, J. (1995). Changes in cluster analysis subtypes among alcoholic
personalities after treatment. Evaluation and the Health Professions, 18(2), 152-165. Ebstein, R.P., & Belmaker, R.H. (1997). Saga of an adventure gene: Novelty seeking,
substance abuse and the dopamine D4 receptor (D4DR) exon III repeat polymorphism. Molecular Psychiatry, 2, 381-384.
Ebstein, R.P., Novick, O., Umansky, R., Priel, B., Osher, Y., Blaine, D., Bennett, E.R. ,
Nemanov, L., Katz, M., & Belmaker, R.H. (1996). Dopamine D4 receptor (D4DR) exon III polymorphism associated with the human personality trait of novelty seeking. Nature Genetics, 12, 78-80.
El-Sheikh, M., Buckhalt, J.A. (2003). Parental Problem Drinking and Children’s
Adjustments: Attachment and Family as Moderators and Mediators of Risk. Journal
of Family Psychology, 17(4), 510-520. Emanuelson, Greg. (2005). SASSI-3 and the court-ordered population: A preliminary
reliability, validity and factor analysis. Dissertation Abstracts International Section
A: Humanities and Social Sciences, 65(7-A), 2575. Embretson, S. (1983). Construct validity: Construct representation versus nomothetic span.
Psychological Bulletin, 93, 179-197. Eysenck, H. J. (1978). Psychopathy, personality & genetics. In R. D. Hare & D. Schalling
(Eds.), Psychopathic behavior: Approaches to research. New York: Wiley. Eysenck, H. J. (1990). Genetic and environmental contributions to individual differences:
The three major dimensions of personality. Journal of Personality, 58, 245-261. Eysenck, H.J. (1991). Dimensions of personality: 16, 5 or 3? Criteria for a taxonomic
paradigm. Personality and Individual Differences, 12(8), 773-790. Eysenck, S.B.G., & Eysenck, H.J. (1975). Manual of the Eysenck Personality Questionnaire.
London, Hodder & Stoughton. Eysenck, H. J., & Eysenck, M. W. (1985). Personality and individual differences: A natural
science approach. New York: Plenum Publishing. Eysenck, S.B.G., Eysenck, H.J., & Barrett, P. (1985). A revised version of the psychoticism
scale. Personality and Individual Differences, 6, 21-29.
116
Eysenck, S.B.G., Pearson, P.R., Easting, G. (1985). Age norms for impulsiveness, venturesomeness, and empathy in adults. Personality and Individual Differences, 6, 613-619.
Feingold, A., Ball, S.A., Krazler, H.R. & Rounsaville, B.J. (1996). Generalizability of the
Type A/ Type B distinction psychoactive substances. American Journal of Drug and
Alcohol Abuse, 22, 449-462. Fillmore, K.M. (1988). Alcohol use across the life course. (Toronto, Ontario, Canada:
Addiction Research Foundation.) Fils-Aime, M.L., Eckardt, M.J., George, G.T., Brown, G.L., Mefford, I., & Linnoila, M.
(1996). Early-onset alcoholics have lower cerebrospinal fluid 5-hydroxyindoleacetic acid levels than late-onset alcoholics. Archives of General Psychiatry, 53, 211-216.
Finn, P.R., Kessler, D.N. & Hussong, A.M. (1994). Risk for alcoholism and classical
conditioning to signals for punishment: Evidence for a weak behavioral inhibition system. Journal of Abnormal Psychology, 103, 293-301.
Finn, P.R., Sharkansky, E.J., Brandt, K.M., & Turcotte, N. (2000). The effects of familial
risk, personality, and expectancies on alcohol use and abuse. Journal of Abnormal
Psychology, 109(1), 122-133. Finn, P.R., Zeitouni, N.C., & Pihl, R.O. (1990). Effects of alcohol on psychophysiological
hyperreactivity to nonaversive and aversive stimuli in men at risk for alcoholism. Journal of Abnormal Psychology, 99(1), 79-85.
Flory, K., Lynam, D., Milich, R., Leukefeld, C. & Clayton, R. (2002). The relations among
personality, symptoms of alcohol and marijuana abuse, and symptoms of comorbid psychopathology: Results from a community sample. Experimental and Clinical
Pharmacology, 10, 425-434. Flory, K., Milich, R., Lynam, D.R., Leukefeld, C., & Clayton, R. (2003). Relation between
childhood disruptive behavior disorders and substance use and dependence symptoms in young adulthood: Individuals with symptoms of attention-deficit/ hyperactivity disorder are uniquely at risk. Psychology of Addictive Behaviors, 17(2), 151-158.
Foster, S. E., Vaughan, R, D., Foster, W. H., & Califano, J. A. (2003). Alcohol consumption
and expenditures for underage drinking and adult excessive drinking. Journal of the
American Medical Association, 28, 989-995. Gfroerer, J. (1987). Correlation between drug use by teenagers and drug use by other family
members. American Journal of Drug and Alcohol Abuse, 13, 95-108.
117
Gianoulakis, C., Krishman, B., & Thavundayil, J. (1996). Enhanced sensitivity of pituitary beta-endorphin to ethanol in subjects at high risk of alcoholism. Archives of General
Psychiatry, 53, 250-257. Glantz, M., & Pickens, R. (1992). Vulnerability to drug abuse. Washington, DC: American
Psychological Association. Goldman, M.S. (2002). College drinking, what it is, and what to do about it: A review of the
state of the science: Introduction. Journal of Studies on Alcohol, 5. Goodwin, D.W. (1968). The genetics of alcoholism, In Gottheil, E., Druley, K.A., Skolada,
T.E., (Eds.), Etiology aspects of alcohol and arug abuse. Springfield, IL. Gotham, H. J., Sher, K. J., & Wood, P. K. (1997). Predicting stability and change in
frequency of intoxication from the college years to beyond: Individual-difference and role transition variables. Journal of Abnormal Psychology, 104(4), 619-629.
Graham, J.R. (2000). MMPI-2: Assessing personality and psychopathology (3
rd ed.). Oxford
Press: New York. Graham, J.R., & Strenger, V. E. (1988). MMPI characteristics of alcoholics: A review.
Journal of Consulting & Clinical Psychology, 56(2), 197-205. Grant, B.F., Harford, T.C., Dawson, D.A., Chou, P., Dufor, M., & Pickering, R. (1994).
Prevalence of DSM-IV alcohol abuse and dependence. Alcohol Health & Research
World, 18, 243-248. Gray, B. Thomas. (2001). A factor analytic study of the Substance Abuse Subtle Screening
Inventory (SASSI). Educational and Psychological Measurement, 61, 102-118. Greene, Ralph E. Jr. (2002). Validity of the Substance Abuse Subtle Screening Inventory-3
for people arrested for driving-under-the influence of alcohol. Dissertation Abstracts
International: Section B: The sciences and Engineering, 63(5-B), 2583. Greene, R. L., Weed, N.C., Butcher, J.N., Arredondo, R., & Davis, H. G. (1992). A cross
validation of MMPI-2 substance abuse scales. Journal of Personality Assessment, 58, 405-410.
Greenfield, L.A. (1998). Alcohol and crime: An analysis of national data on the prevalence of
alcohol involvement in crime (PDF-228K). Report prepared for the Assistant
Attorney General’s National Symposium on Alcohol Abuse and Crime. Washington, DC: U.S. Department of Justice.
Griffin, K.W., Botvin, G.J., Scheier, L. M., & Nichols, T. R. (2002). Factors associated with
regular marijuana use among high school students: A long-term follow-up study. Substance Use and Misuse, 37, 225-238.
118
Gynther, L.M., Carey, G., Gottesman, I.I., & Vogler, G.P. (1994). A twin study of non-alcoholic substance abuse. Psychiatry Research, 56, 213-220.
Stratham, D.J., Dunne, M.P., Whitfield, J.B., & Martin, N.G. (1997). Genetic and environmental contributions to alcohol dependence risk in a national twin sample: Consistency of findings in women and men. Psychological Medicine, 27, 1381-1396.
Helzer, J.E., Burnam, A., & McElvoy, L.T. (1991). Alcohol abuse and dependence. In L.N.
Robins & D.A. Reiger (Eds.), Psychiatric disorders in America: The epidemiologic
catchment area study (pp. 81-115). New York: The Free Press. Helzer, J.E., & Pryzbeck, T.R. (1988). The co-occurrence of alcoholism with other
psychiatric disorders in the general population and its impact on treatment. Journal
of Studies on Alcohol, 49(3), 219-224. Hennecke, L. (1984). Stimulus augmenting and field dependence in children of alcoholic
fathers. Journal of Studies on Alcohol, 45(6), 486-492. Hesselbrock, M.N. (1991). Gender comparison of antisocial personality disorder and
Structure of the NEO Five-Factor Inventory: Construct Validity for the Big Four Personality Clusters. Canadian Journal of Behavioral Science, 38(1), 24-40.
Horvath, P., & Zuckerman, M. (1993). Sensation seeking, risk appraisal, and risky behavior.
Personality and Individual Differences, 14, 41-52. Howard, M. O., Kivlahan, D., & Walker, R. D. (1997). Cloninger’s tridimensional theory of
personality and psychopathology: Applications to substance use disorders. Journal of
Studies on Alcohol, 58(1), 48-66.
119
Hur, Y.M., McGue, M., & Iacono, W.G. (1998). The structure of self-concept in female preadolescent twins: A behavioral genetic approach. Journal of Personality and
Social Psychology, 74(4), 1069-1077. Iacono, W.G., Carlson, S.R., Taylor, J., Elkins, I.J., & McGue, M. (1999). Behavioral
disinhibition and the development of substance-use disorders: Findings from the Minnesota twin family study. Development and Psychopathology, 11, 869-900.
Institute of Medicine. (2006). Reducing underage drinking: A collective responsibility.
Washington, DC: National Academy of Sciences. Jackson, C.P., & Matthews, G. (1998). The prediction of habitual alcohol use from alcohol
related expectancies and personality. Alcohol and Alcoholism, 23, 305-314. Jacob, Theodore, Windle, Michael, Seilhamer, Ruth Ann & Bost, Jim. (1999). Adult children
of alcoholics: Drinking, psychiatric and psychosocial status. Psychology of Addictive
Behaviors, 13 (1), 3-21. Jang, K.L., Livesley, W.J., & Vernon, P.A. (1995). Alcohol and drug problems: A
Multivariate behavioral genetic analysis of co-morbidity. Addiction, 90, 1213-1221. Jang, K.L., Livesley, W.J., Vernon, P.A. (1998). The relationship between Eysenck’s P-E-N
model of personality and traits delineating personality disorder. Personality and
Individual Differences, 26, 121-128. Jellinek, E.M. (1960). The disease concept of alcoholism. New Haven, CT: College &
University Press. Jessor, R., Donovan, J.E., & Costa, F.M. (1991). Beyond adolescence: Problem behavior and
young adult development. New York: Cambridge University Press. Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (1994). National survey results on drug
use from the Monitoring the Future Study, 1975-1993. Volume II: College students
and young adults (NIH Publication No. 94-3810). Rockville, MD: National Institute on Drug Abuse.
Johnston, L. D., O’Malley, P. M., & Bachman, J. G. (1998). National survey results on drug
use from the Monitoring the Future Study, 1975-1997. Volume II: College students
and young adults ((NIH Publication No. 98-4346). Rockville, MD: National Institute on Drug Abuse.
120
Kahler, C.W., Read, J.P., Wood, M.D., Palfai, T.P. (2003). Social environmental selection as a mediator of gender, ethnic, and personality of college student drinking. Psychology
of Addictive Behaviors, 17(3), 226-234. Kasl, S.V., Ostfeld, A.M., Berkman, L.F., & Jacobs, S.C. (1987). Stress and alcohol
consumption. The role of selected social and environmental factors. In E. Gotheil, K.A. Druley, S. Pashko, & S.P. Weinstein (Eds.), Stress and Addiction (pp. 40-60). New York: Bruner/Mazel.
Kelly, J.P., Kaufman, D.W., Koff, R.S., Laszlo, A., Wilholm B.E. & Shapiro,S. (1995).
Alcohol consumption and the risk of major upper gastrointestinal bleeding. American
Journal of Gastroenterol, 90(7), 1058-1064. Kendler, K.S., Heath, A.S., Neale, M.C., Kessler, R.C., & Eaves, L.J. (1992). A population
based twin study of alcoholism in women. Journal of the American Medical
alcohol intake in pregnancy and the risk of spontaneous abortion. Alcohol &
Alcoholism, 37(1), 87-92. Kessler, R.C., Crum, R.M., Warner, L.A., Nelson, C.B., et al (1997). Lifetime co-occurrence
of DSM-III-R alcohol abuse and dependence with other psychiatric disorders in the National Comorbidity Survey. Archives of General Psychiatry, 54(4), 313-321.
Kessler, R.C., Sonnega, A., Bromet, E., Hughes, M., & Nelson, C.B. (1995). Posttraumatic
stress disorder in the National Comorbidity Survey. Archives of General Psychiatry,
52, 1048-1060. Kibley, M.M., Downey, K., & Breslau, N. (1998). Predicting the emergence and persistence
of alcohol dependence in young adults: The role of expectancy and other risk factors. Experimental and Clinical Psychopharmacology, 6, 149-156.
King, Serena M., Burt, S. Alexandra, Malone, Stephen M., McGue, Matt & Iacono, William
G. (2005). Etiological contributions to heavy drinking from late adolescence to young adulthood. Journal of Abnormal Psychology, 114 (40), 587-598.
121
Knight, J.R., Wechsler, H., Kuo, M. Seibring, M., Weitzman, E. R., & Schuckit, M.A. (2002). Alcohol abuse and dependence among U.S. college students. Journal of
Studies on Alcohol, 63 (3), 263-270. Knight, R.P. (1937). The dynamics and treatment of chronic alcohol addiction. Bulletin of the
Menninger Clinic, 1, 233-250. Kochanek, K.D., Murphy, S.C., Anderson, R.N., & Scott, C. (2004). Deaths: Final data for
2002 (PDF-9.5Mb). National Vital Statistics Reports, 53(5), National Center for Health Statistics, Hayattsville, MD.
Kollins, S.H. (2003). Delay discounting is associated with substance use in college students.
Etiologic connections among substance dependence, antisocial behavior, and personality: Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411-424.
Kushner, M.G., Sher, K.J., & Beitman, B.D. (1990). The relation between alcohol problems
and anxiety disorders. American Journal of Psychiatry, 147, 685-695. Labouvie, E.W., & McGee, C. (1986). Relation of personality to alcohol and drug use in
adolescence. Journal of Consulting and Clinical Psychology, 54(3), 289-293. Lacey, J.H., & Evans, C.D.H. (1986). The impulsivist: A multi-impulsive personality
disorder. British Journal of Addictions, 81, 641-649. Lawford, B.R., Young, R.M., Rowell, J.A., Gibson, J.N., Feeney, G.F.X., Ritchie, T.L.,
Syndulko, K., & Noble, E.P. (1997). Association of the D2 Dopamine receptor A1 allele with alcoholism: Medical severity of alcoholism and type of controls. Biological Psychiatry, 41, 386-393.
Lazowski, Linda E., Miller, Franklin G., Boye, Michael W. & Miller Glenn A. (1998).
Efficacy of the Substance Abuse Subtle Screening Inventory-3 (SASSI-3) in identifying substance dependence disorders in clinical settings. Journal of
Personality Assessment, 71 (1), 114-128.
122
Lejuez, C.W., Read, J.P., Kahler, C.W., Richards, J.B., Ramsey, S.E., Stuart, G.L., et al. (2002). Evaluation of a behavioral measure of risk-taking: The Balloon Analogue Risk Task (BART). Journal of Experimental Psychology, 8, 75-84.
Lennon, R. T. (1956). Assumptions underlying the use of content validity. Educational and
Psychological Measurement, 16, 294-304. Leonard, K.E., & Rothbard, J.C. (1999). Alcohol and the marriage effect. Journal of Studies
of Alcohol Supplemental,13,139-146. Lesher, S.D.H., & Lee, Y.T.M. (1989). Acute pancreatitis in a military hospital. Military
Medicine, 154(11), 559-564. Lisman, S.A. (1987). Alcohol and human stress: Closer to the truth or time to go ask some
new questions? In E. Gotheil, K.A. Druley, S. Pashko, & S.P. Weinstein (Eds.), Stress and addiction (pp. 61-71). New York: Brunet/ Mazel.
Loevinger, J. (1957). Objective tests as instruments of psychological theory (Monograph).
substance abuse and psychopathology among siblings of opioid abusers. Journal of
Nervous and Mental Disease, 180, 153-161. MacAndrew, C. (1965). The differentiation of male alcoholic outpatients from nonalcoholic
psychiatric outpatients by means of the MMPI. Quarterly Journal of Studies on
Alcohol, 26, 238-246. MacAndrew, C. (1979). Evidence for the presence of two fundamentally different, age
independent characterological types within unselected runs of male alcohol and drug abusers. American Journal of Drug and Alcohol Abuse, 6, 207-221.
MacAndrew, C. (1979). On the possibility of the psychometric detection of persons who are
prone to the abuse of alcohol and other substances. Addictive Behaviors, 4, 11-20. MacAndrew, C. (1980). Male alcoholics, secondary psychopathy and Eysenck’s theory of
personality. Personality and Individual Differences, 1, 151-160. MacAndrew, C. (1981). What the MAC scale tells us about male alcoholics: An interpretive
review. Journal o f Studies on Alcohol, 42, 604-625. MacAndrew, C. (1983). Alcoholic personality or personalities: Scale and profile data from
the MMPI. In W. M. Cox (Ed.), Identifying and measuring alcoholic personality
characteristics (pp. 73-85). New directions for Methodology of Social and Behavioral Sciences, no. 16. San Francisco: Jossey-Bass.
123
Macaskill, G.T., Hopper, J.L., White, V., & Hill, D.J., (1994). Genetic and environmental variation in Eysenck personality questionnaire scales measured on Australian adolescent twins. Behavioral Genetics, 24(6), 481-491.
Madden, G. J., Bickel, W.K., & Jacobs, E.A. (1999). Discounting of delayed rewards in
opioid-dependent outpatients: Exponential or hyperbolic discounting functions? Experimental and Clinical Psychopharmacology, 7, 284-293.
Magnusson, D., & Bergman, L.R. (1990). A pattern approach to the study of pathways from
childhood to adulthood. In L. Robins & M. Rutter (Eds.), Straight and devious
pathways from childhood to adulthood (pp. 101-115). Cambridge, England: Cambridge University Press.
Mann, Leon (1992). Stress, affect, and risk taking. John Wiley & Sons, XXII, 345. Marin, E.D., & Sher, K.J. (1994). Family history of alcoholism, alcohol use disorders and the
five-factor model of personality. Journal of Studies on Alcohol, 55(1), 81-90. Martin, E.D., & Sher, K. J. (1994). Family history of alcoholism, alcohol use disorders and
the five-factor model of personality. Journal of Studies on Alcohol, 55(1), 81-90. Martsh, C.T., & Miller, W.R. (1997). Extroversion predicts heavy drinking in college
students. Personality and Individual Differences, 23(1), 153-155. Masse, L.C., & Tremblay, R.E. (1997). Behavior of boys in kindergarten and the onset of
substance use during adolescence. Archives of Genetic Psychiatry, 54, 62-68. Matano, R.A., Locke, K.D., & Schwartz, K. (1994). MCMI personality subtypes for male and
female alcoholics. Journal of Personality Assessment, 63(2), 250-264. Mayfield, D., McLeod, G., & Hall, P. (1974). The CAGE questionnaire: Validation of a new
alcoholism screening instrument. American Journal Of Psychiatry, 131, 1121-1123. McCord, J. (1990). Long-term perspectives on parental absence. In L. Robins & M. Rutter
(Eds.), Straight and devious pathways from childhood to adulthood (pp. 116-134). Cambridge, England: Cambridge University Press.
McCown, W.G. (1988). Multi-impulsive personality disorder and multiple substance abuse:
evidence from members of self-help groups. British Journal of Addiction, 83, 431-432.
adulthood. Boston: Little Brown. McCrae, R.R., & Costa, P.T. (1991). The NEO Personality Inventory: Using the Five-Factor
Model in counseling. Journal of Counseling & Development, 69(4), 367-372.
124
McCrae, R.R., & Costa, P.T., Jr. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52, 81-90.
McCrae, R.R., & Costa, P.T., Jr. (1985b). Openness to experience. In R. Hogan & W. H.
Jones (Eds). Perspectives in personality, (Vol 1, pp.145-172). Greenwich, CT: JAI Press.
McCrae, R.R., & Costa, P.T., Jr. (1985a). Comparison of EPI and psychoticism scales with
measures of the five-factor model personality. Personality and Individual
Differences, 6, 587-597. McCrae, R.R., & Costa, P.T., Jr. (1989). The structure of interpersonal traits: Wiggins’s
circumplex and the five-factor model. Journal of Personality and Social Psychology,
56, 586-595. McCrae, R.R., & Costa, P.T., Jr. (1987). Validation of the five-factor model of personality
across instruments and observers. Journal of Personality and Social Psychology, 52, 81-90.
McCrae, R.R., & Costa, P.T., Jr. (1989). The structure of interpersonal traits: Wiggins’s
circumplex and the five-factor model. Journal of Personality and Social Psychology,
56, 586-595. McGinnis, J. M., & Foege, W. H. (1993). Actual causes of death in the United States. Journal
of Actuaries, 270, 2207-2212. McGue, M., Slutske, W., & Iacono, W.G. (1999). Personality and substance use disorder: II.
Alcoholism versus drug use disorders. Journal of Consulting and Clinical
Psychology, 67, 394-404. Merta, Rod J. (2001). Addictions counseling. Counseling and Human Development, Jan, 1-
39.
Messick, S. (1964). Personality measurement and college performance. In Proceedings of the
Messick, S. (1975). The standard problem: Meaning and values in measurement and
evaluation. American Psychologist, 30, 955-966. Messick, S. (1980). Test validity and the ethics of assessment. American Psychologist, 35,
1012-1027. Messick, S. (1989a). Meaning and values in test validation: The science and ethics of
assessment. Educational Researcher, 23(2), 13-23. Messick, S. (1989b). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13-
103). New York: Macmillan.
125
Messick, Samuel. (1995). Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, Sept., 741-749.
Messick, Samuel. (1995). Standards of validity and the validity of standards in performance
assessment. Educational Measurement: Issues and Practice, Winter, 5-8. Miller, D., & Blum, K. (1996). Overload: Attention deficit disorder and the addictive brain.
Kansas City: Andrews and McMeel. Miller, F.G., & Lazowski, L.E. (1999). The SASSI-3 manual. SASSI Institute: Springville,
Bloomington, IN: Baugh Enterprises, Inc. Miller, G. A. (1985). The substance abuse subtle screening inventory (SASSI) manual.
Spencer, IN: Spencer Evening World. Miller, G. A. (1994). The substance abuse subtle screening inventory (SASSI): Adult SASSI-2
manual supplement. Spencer, IN: Spencer Evening World. Miller, G.A. (1997). The substance abuse subtle screening inventory (SASSI-3): Manual.
SASSI Insititute: Bloomington, IN. Miller, W.R., Tonigan, J.S., & Longabaugh, R. (1995). The drinker inventory of
consequences (DRInC): An instrument for assessing adverse consequences of alcohol abuse: Test manual. National Institute on Alcohol Abuse and Alcoholism,
Project MATCH series, 4 NIH Publication No. 95-3911, U.S. Government Printing Office, Washington, DC.
Mokdad, A.H., Marks, J.S., Stroup, D.F., & Gerberding, J.L. (2004). Actual causes of death
in the States, 2000. JAMA, 291(10), 1238-1245. Moreland, K.L. (1985b). Test-retest reliability of 80 MMPI scales. Unpublished materials,
(Available from National Computer Systems, Minneapolis, MN). Morey, L.C., & Skinner, H.A. (1986). Empirically derived classifications of alcohol-related
problems. In Galanter, M. (Eds.) Recent development in alcoholism, (pp. 144-168). New York: Plenum Press.
Musgrave-Marquart, D., Bromley, S.P., & Dalley, M.B. (1997). Personality academic
attribution, and substance use as predictors of academic achievement in college students. Journal of Social Behavior & Personality, 12(2), 501-511.
126
Naimi, T. S., Brewer, D., Mokdad, A., Denny, C., Serdula, M. K., & Marks, J. S. (2003). Binge drinking among U.S. adults. Journal of the American Medical Association,
289, 70-75. Nathan, P.E. (1988). The addictive personality is the behavior of the addict. Journal of
Consulting and Clinical Psychology, 56, 183-189. National Institute on Alcohol Abuse and Alcoholism. (1995).Assessing alcohol problems: A
guide for clinicians and researchers (NIH Publication No. 953745, U.S. Government Printing Office, Bethesda, MD.
National Institute on Alcohol Abuse and Alcoholism. (2000). 10
th annual report to the U.S.
Congress on alcohol and health. Washington, DC. National Institute on Alcohol Abuse and Alcoholism. (2000). The alcohol and other drug
(AOD) thesaurus: A guide to concepts and terminology in substance abuse and
addiction (3rd
ed.). Bethesda, JD: Author. National Institute of Alcohol Abuse and Alcoholism (2004). Percent reporting alcohol use in
the year by age group and demographic characteristics: NHDA, 1994-97. National Institute of Alcohol Abuse and Alcoholism (2004). Percent who drink beverage
alcohol, by gender, 1939-2000. Nelson, T. F., Naimi, T. S., & Wechsler, H. (2005). The State sets the rate: The relationship
of college binge drinking to state binge drinking rates and selected state alcohol control policies. American Journal of Public Health, 95(3), 441-446.
relationship between personality and DSM-III axis I disorders in the population: Results from an epidemiological survey. American Journal of Psychiatry, 149(9), 1228-1233.
Newcomb, M.D., & Bentler, P. (1990). Antecedents and consequences of cocaine use: An
eight-year study from early adolescence to young adulthood. In L. Robbins & M. Rutter (Eds.), Straight and devious pathways from childhood to adulthood (pp. 158-181). Cambridge, England: Cambridge University Press.
Newcomb, M.D., & McGee, L. (1991). The influence of sensation seeking on general and
specific problem behaviors from adolescence to young adulthood. Journal of
Personality and Social Psychology, 61, 614-628. Newport, F. (1999). More than a third of Americans report drinking has caused family
problems. Gallup News Service. Noble, E.P. (1996). The gene that rewards alcoholism. Scientific American, 52-61.
127
Noble, E.P., Blum, B., Khalsa, M.E., Ritchie, T., Montgomery, A.,Wood, R.C., Fitch, R. J., Ozkaragoz, T., Sheridan, P.J., Anglin, M.D., Paredes, A., Treiman, L.J., & Sparkes, R.S. (1993). Allelic association of the D2 dopamine receptor gene with cocaine dependence. Drug and Alcohol Dependence, 33, 271-285.
Noble, E.P., St. Jeor, S.,T., Ritchie, T., Syndulko, K., St. Jeor, S.C., Fitch, R.J., Brunner,
R.L., & Sparkes, R.S. (1994). D2 dopamine receptor gene and cigarette smoking. A reward gene? Medical Hypothesis, 42, 257-260.
O’Boyle, M., & Barratt, E.S. (1993). Impulsivity and DSM-III-R personality disorders.
Personality and Individual Differences, 14, 609-611. O’Brien, C.P., Eckart, M.J., & Linnoila, M.I. (1995). Pharmacotherapy of alcoholism. In F.E.
Bloom and D.J. Kupfer (Eds.), Psychopharmacology: The fourth generation of
progress (pp. 1745-1755). New York: Raven Press. Office of Applied Studies, Substance Abuse and Mental Health Services Administration
(1998). National household survey on drug abuse, Rockville,MD: SAMHSA. Office of Applied Studies. (2004). The NSDUH report: Alcohol dependence or abuse and
use. Rockville, MD: Substance Abuse and Mental Health Services Administration. O’Malley, P. M., & Johnston, L. D. (2002). Epidemiology of alcohol and other drug use
among American college students. Journal of Studies on Alcohol, S14, 23-29. O’Neil, P.M., Giacinto, J.P., Waid, L.R., Roitzsch, J.C., Miller, W.C., & Kilpatrick, D.G.
(1983). Behavioral, psychological, and historical correlates of MacAndrews scale scores among male alcoholics. Journal of Behavioral Assessment, 5, 261-273.
O’Neill, Susan E., Parra, Gilbert, R., & Sher, Kenneth J. (2001). Clinical relevance of heavy
drinking during the college years: Cross-sectional and prospective perspectives. Psychology of Addictive Behaviors, 15(4), 350-359.
Peele, S. (1993).The conflict between public health goals and the temperance mentality.
American Public Health, 83, 805-810. Perkins, H.W. (2002). Surveying the damage: A review of research on consequences of
alcohol misuse in college populations. Journal of Studies on Alcohol, 14, 91-100. Piazza, N. J., Martin, N., Dildine, R.J. (2000). Screening instruments for alcohol and other
drug problems. Journal of Mental Health Counseling, 22 (3), 218-227. Pickens, R.W., Svikis, D. S., McGue, M., Lykken, D. T., Heston, L.L., & Clayton, P. J.
(1991). Heterogeneity in the inheritance of alcoholism: A study of male and female twins. Archives of General Psychiatry, 48, 19-28.
128
Piko, B. (2001). Gender differences and similarities on adolescents’ ways of coping. Psychological Records, 51, 223-235.
Pokorny, A.D., Miller, B.A., & Kaplin, H.B. (1972). The brief MAST: A shortened version
of the Michigan alcoholism screening test. American Journal of Psychiatry, 129, 342-345.
Pomery, Elizabeth A., Gibbons, Frederick X., Gerrard, Meg, Cleveland, Michael J., Brody,
Gene H., & Wills, Thomas A. (2005). Families and risk: Prospective analyses of familial and social influences on adolescent substance use. Journal of Family
Psychology, 19 (4), 560-570. Prescott, C.A., Hewitt, J.K., Truett, K.R., Heath, A.C., et al. (1994). Genetic and
environmental influences on a lifetime alcohol-related problems in a volunteer sample of older twins. Journal of Studies of Alcohol, 55(2), 184-202.
Pulkkinen, L., & Pitkanen, T. (1994). A prospective study of the precursor to problem
drinking in young adulthood. Journal of Studies on Alcohol, 55(5), 578-587. Rajan, K.B., Leroux, B.G., Peterson, A.V. Bricker, J.B., Andersen, M.R., Kealey, K.A., et.al.
(2003). Nine year prospective association between older siblings’ smoking and children’s daily smoking. Journal of Adolescent Health, 33, 25-30.
Rankin, H., Stockwell, T., & Hodgson, R. (1982). Personality and alcohol dependence.
R., Stouthhamer-Loeber, M., & Green, S. (1991). Preliminary development of a sensation seeking scale for children. Personality and Individual Differences, 12, 399-405.
Rydelius, P.A. (1981). Children of alcoholic fathers: A longitudinal prospective study
An empirical basis for the primary prevention of psychosocial disorders (pp. 296-
297). New York: Oxford University Press. Sanap, M., & Chapman, M.J. (2003). Severe ethanol poisoning: a case report and brief
review. Critical Care Resuscitation, 5(2), 106-108. Sawrie, S.M., Kabat, M.H., Dietz, C.B., Greene, R.L., Arredondo, R., & Mann, A.W. (1996).
Internal structure of the MMPI-2 Addiction Potential scale in alcoholic and psychiatric inpatients. Journal of Personality Assessment, 66, 177-193.
Scheidt, D.M., & Windle, M. (1997). A Comparison of Alcohol Typologies Using HIV Risk
Behaviors Among Alcoholic Inpatients. Psychology of Addictive Behaviors, 11(1), 3-17.
Schiff, E.R. (1997). Hepatitis C and alcohol. Hepatology, 26(Suppl 1), 39S-42S. Schinka, J.A. (1995). PAI profiles in alcohol-dependent patients. Journal of Personality
Assessment, 65(1), 35-51.
130
Schuckit, M.A. (1988). A search for biological markers in alcoholism: Applications to psychiatric research. In R.M. Rose & J.E. Barrett (Eds.), Alcoholism: Origins and
outcome (pp. 143-154). New York: Raven Press. Schuckit, M.A., Irwin, M., & Brown, S.A. (1990). The history of anxiety symptoms among
171 primary alcoholics. Journal of Studies on Alcohol, 51(1), 34-41. Schuckit, M.A., & Smith, T.L. (1996). An 8-year follow-up of 450 sons of alcoholic and
control subjects. Archives of General Psychiatry, 53, 202-210. Schulenberg, J. (2000). “I’m not a drunk, just a college student”: Binge drinking during
college as a developmental disturbance. Paper presented at the annual meeting of the Research Society on Alcoholism, Denver, CO.
Selzer, M. L. (1971). The Michigan Alcoholism Screening Test: The quest for a new
diagnostic instrument, American Journal of Psychiatry, 127, 89-94.
Sheppard, D., Smith, G.T., & Rosenbaum, G. (1988). Use of MMPI subtypes in predicting completion of a residential alcoholism treatment program. Journal of Consulting and
Clinical Psychology, 56, 590-596.
Sher, K. (1993). Children of alcoholics and the intergenerational transmission of alcoholism: A biopsychosocial perspective. In J.S. Baer, A. Marlatt & R. J. McMahon (Eds.), Addiction behaviors across the life span (pp. 3-33.). Newbury Park, NJ: Sage.
Sher, K.J., Bartholow, B.D., & Wood, M.D. (2000). Personality and substance use disorders:
A prospective study. Journal of Consulting Psychology, 68, 818-829. Sher, K.J. & Gotham, H.J. (1999). Pathological alcohol involvement. A developmental
disorder of young adulthood. Development and Psychopathology, 11, 933-956. Sher, K.J., & Trull, T. J. (1994). Personality and disinhibitory psychopathology: Alcoholism
and antisocial personality. Journal of Abnormal Psychology, 103, 92-102. Sher, K. J., Trull, T. J., Bartholow, B. D., & Vieth, A. (1999). Personality and alcoholism:
Issues, methods and etiological processes. In K. Leonard & H. Blaine (Eds.), Psychological theories of drinking and alcohol (2nd
ed., pp. 54-105). New York: Guilford.
131
Sher, K.J., Walitzer, K.S., Wood, P.K., & Brent, E.E. (1991). Characteristic of children of alcoholics: Putative risk factors, substance use and abuse, and psychopathology. Journal of Abnormal Psychology, 100, 427-448.
SPSS. (2001). SPSS for Windows-Release 11.0.1. Chicago, IL: SPSS.
Stabenau, J.R. (1992). Is risk for substance abuse unitary? Journal of Nervous and Mental
Disorders, 180, 583-588. Sutker, P.B., & Allain, A.N. (1988). Issues in personality conceptualizations of addictive
behaviors. Journal on Consulting Psychology, 56, 172-182. Tabakoff, B., & Hoffman, P.L. (1987). Biochemical psychopharmacology of alcohol. In H.Y.
Meltzer (Ed.), Psychopharmacology: The third generation of progress (pp. 1521-1526). New York: Raven Press.
Target, M. (1998). Outcome research on the psychosocial treatment of personality disorders.
Bulletin of the Meninger Clinic, 62, 215-230. Tarter, R.E. (1998). Are there inherited behavioral traits that predispose to substance abuse?
Journal of Consulting and Clinical Psychology, 56, 189-196. Tarter, R. E., & Hegedus, A.M. (1991). Drug use screening inventory: Its application in the
evaluation and treatment of alcohol and other drug abuse. Alcohol Health Research
World, 15(1), 65-75. The National Center on Addiction and Substance Abuse at Columbia University.(1999). No
safe haven: Children of substance-abusing parents (PDF-989K). The SASSI Institute.(1998).The Reliability and Validity of the SSSI-3. The SASSI Institute:
Toomey, R., Faraone, S.V., & Eaves, L. (1996). Genetic influences on DSM-III-R drug abuse and dependence: A study of 3,372 twin pairs. American Journal of
Medical Genetics (neuropsychiatric Genetics), 67, 473-477.
133
Tucker, J.S., Friedman, H.S., Tomlinson-Keasey, C., Schwartz, J.E., et al. (1995). Childhood psychosocial predictors of adulthood smoking, alcohol consumption, and physical activity. Journal of Applied Social Psychology, 25(21), 1884-1899.
U.S. Department of Agriculture, U.S. Department of Health and Human Services. (2003).
Dietary guidelines for Americans. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention.
(2004). National health interview survey. National Center for Health Statistics, Hyattsville, MD.
U.S. Department of Health and Human Services, Substance Abuse and Mental Health
Services Administration, Office of Applied Studies. (2004). Drug Abuse Warning
Network 2003: Interim national estimates of drug-related emergency department
visits.
U.S. Department of Justice Office of Justice Programs, Office of Juvenile Justice and
Delinquency Prevention. (1999). Drinking in America: Myths, realities and prevention policy. National Household Survey Data, Calverton, MD: Pacific Institute for Research and Evaluation.
Uhl, G.R. Persico, A.M., & Smith, S.S. (1992). Current excitement with D2 dopamine
receptor gene alleles in substance abuse. Archives of General Psychiatry, 49, 157-160.
Vaillant, G.E. (1996). A long-term follow-up of male alcoholic abuse. Archives of General
Psychiatry, 53, 243-249.
Vaillant, G.E., & Milofsky, E.S. (1982). The etiology of alcoholism: A prospective viewpoint. American Psychologist, 27(5), 494-503.
Vando, A. (1969). A personality dimension related to pain tolerance. (Doctoral dissertation. Columbia University).
Verheul, R., Hartgers, C., van den Brink, W., & Koeter, M.W.J.(1998). The effect of
sampling, diagnostic criteria and assessment procedures on the observed prevalence of DSM-III-R personality disorders among treated alcoholics. Journal of Studies on
Alcohol, 59, 227-236. Vink, J. M., Willensen, G., & Boomsma, D.I. (2003). The association of current smoking
behavior with the smoking behavior of parents, siblings, friends, and spouses. Addiction, 98, 923-931.
134
von Knorring, L., Oreland, L., & von Knorring, A-L. (1987). Personality traits and platelet MAO activity in alcohol and drug abusing teenage boys. Acta psychiatrica
Scandinavica, 75, 307-314.
von Knorring, L., Palm, U., & Anderson, H. (1985). Relationship between treatment outcome
and subtype of alcoholism in men. Journal of Studies on Alcohol, 46(5), 388-391. Wagner, M.K. (2001). Behavioral characteristics related to substance abuse and risk-taking,
sensation-seeking, anxiety sensitivity, and self-reinforcement. Addictive Behaviors,
26(1), 115-120. Waldeck, T.L., & Miller, L.S. (1997). Gender and impulsivity differences in licit substance
use. Journal of Substance Abuse, 9, 269-275. Watson D., & Clark, L. A. (1993). Behavioral disinhibition versus constraint: A dispositional
perspective. In D. M. Wegner & J. W. Pennebaker, Handbook of mental control (Century psychology series, pp. 506-527). Englewood Cliffs, NJ: Prentice-Hall.
Wechsler, Henry, Dowdall, George W., Davenport, Andrea & Castillo, Sonia (1995). Correlates of college student binge drinking. American Journal of Public Health, 85 (7), 921-
927. Wechsler, H., & Isaac, N. (1992). Binge drinkers at Massachusetts colleges: Prevalence ,
drinking styles , time trends and associated problems, Journal of the American
Medical Association, 267, 2929-2931. Wechsler, H., Lee, J. E., Kuo, M. & Lee, H. (2000). College binge drinking in the 1990s: A
continuing problem. Results of the Harvard school of public health 1999 college alcohol study. Journal of American College Health, 48, 199-210.
Weed, N.C., Butcher, J.N., McKenna, T., & Ben-Porath, Y.S. (1992). New measures for
assessing alcohol and drug abuse with the MMPI-2: The APS and AAS. Journal of
Personality Assessment, 58, 389-404. Widiger, T.A., & Clark, L.A. (2000). Toward DSM-V and the classification of
psychopathology. Psychological Bulletin, 126, 946-963. Windle, M. (2000). Parental, sibling, and peer influences on adolescent substance use of
Wolf, A.W., Schubert, D.S.P., Patterson, M., Grande, T., & Pendelton, L. (1990). The use of the MacAndrew Alcoholism scale in detecting substance abuse and anti-social personality. Journal of Personality Assessment, 54, 747-755.
Wood, M.D., Read, J.P., Mitchell, R.E., & Brand, N.H. (2004). Do Parents Still Matter?
Parent and Peer Influences on Alcohol Involvement and High School Graduates. Psychology of Addictive Behaviors, 18(1), 19-30.
environmental influences on behavioral disinhibition. American Journal of Medical
Genetics (Neuropsychiatric Genetics), 96, 684-695. Zucker, R.A., Ellis, D.A., Bingham, C.R., Fitzgerald, H.E., & Sanford, K. (1996). Other
evidence for at least two alcohol types and course variation in antisociality and heterogeneity of alcoholic outcome. Development & Psychopathology, 8, 831-848.
Zucker, R.A., Fitzgerald, H.E., & Moses, H.D. (1995). Emergence of alcohol problems and
the several alcoholisms: A developmental perspective on etiologic theory and life course trajectory. In D. Cicchetti & D.J. Cohen (Eds.), Developmental
psychopathology: Vol. 2. Risk, disorder, and adaptation (pp. 677-711). New York: Wiley.
Zuckerman, M. (1969). Theoretical formulations. In J.P. Zubek (Ed.), Sensory deprivation:
Fifteen years of research (pp. 407-432). New York: Appleton-Century. Zuckerman, M. (1979). Sensation seeking: Beyond the optimal level of arousal. Hillsdale, NJ:
Erlbaum. Zuckerman, M. (1983b). A biological theory of sensation seeking. In M. Zuckerman (Ed.),
Biological Bases of Sensation Seeking, Impulsivity and Anxiety (pp.37-76), Hillsdale, NJ: Erlbaum.
Zuckerman, M. (1983f). Biological Bases of Sensation Seeking, Impulsivity and Anxiety,
Hillsdale, NJ: Erlbaum. Zuckerman, M. (1986). Sensation seeking and the endogenous deficit theory of drug abuse.
National Institute of Drug Abuse Research Monographs Series, 74, 59-70. Zuckerman, M. (1987). Biological connection between sensation seeking and drug abuse. In
J. Engel & L. Oreland (Eds.), Brain reward systems and abuse. New York: Raven Press.
Zuckerman, M. (1987). Is sensation seeking a predisposing trait for alcoholism? In E.
Gottheil, K.A. Druley, S. Pashkey, & S.P. Weinstein (Eds.), Stress and addiction (pp. 283-301). New York: Bruner/ Mazel.
136
Zuckerman, M. (1989). Personality in the third dimension: A psychobiological approach. Personality and Individual Differences, 10, 391-418.
Zuckerman, M. (1990; 1990a). The psychophysiology of sensation seeking. Journal of
Personality, 58, 313-345. Zuckerman, M. (1991). Psychobiology of personality. Cambridge, England: Cambridge
University Press. Zuckerman, M. (1993). P-impulsive sensation seeking and its behavioral,
psychophysiological biochemical correlates. Neuropsychobiology, 28, 30-36. Zuckerman, M. (1994; 1994b). Behavioral expressions and biosocial bases of sensation
seeking. New York: Cambridge University Press. Zuckerman, M. (2003). Behavioral expressions and biosocial bases of sensation seeking,
New York: Cambridge University Press. Zuckerman, M., & Cloninger, C.R. (1996). Relationships between Cloninger’s, Zuckerman’s,
and Eysenck’s dimension. Personality and Individual Differences,21, 283-285. Zuckerman, M., Kolin, I., Price, L., & Zoob, I. (1964). Development of a sensation seeking
scale. Journal of Consulting Psychology, 28, 477-482. Zuckerman, M., & Kuhlman, D. M. (2000). Personality and risk taking: Common biosocial
factors. Journal of Personality, 68, pp. 999-1029. Zuckerman, M., Kuhlman, D.M., & Camac, C. (1988). What lies beyond E and N? Factor
analysis of scales believed to measure basic dimensions of personality. Journal of
Personality and Social Psychology, 54, 96-107. Zuckerman, M., Kuhlman, D.M., Joireman, J. Teta, P. & Kraft, M. (1993). A comparison of
three structural models of personality: The big three, the big five, and the alternative five. Journal of Personality and Social Psychology, 65, 757-768.
Zuckerman, M., Kuhlman, D.M., Thornquist, M., & Kiers, H. (1991). Five (or three) robust
questionnaire scale factors without culture. Personality and Individual Differences,
23, 929-941. Zuckerman, M., Murtaugh, T.T., & Siegel, J. (1974). Sensation seeking and cortical
augmenting-reducing. Psychophysiology, 11, 535-542. Zuckerman, M., Murtaugh, T.T., & Siegel, J. (1974). Sensation seeking and cortical