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MODIFYING THE CAFFEINE CONSUMPTION QUESTIONNAIRE:
IMPULSIVITY AND EXPECTANCIES AS PREDICTORS OF CAFFEINE CONSUMPTION
Jennifer Ashley Heaton
A Thesis Submitted to the
University of North Carolina Wilmington in Partial Fulfillment
of the Requirements for the Degree of
Master of Arts
Department of Psychology
University of North Carolina Wilmington
2012
Approved by
Advisory Committee
Len Lecci Richard L. Ogle
Nora E. Noel
Chair
Accepted by
Dean, Graduate School
ii
TABLE OF CONTENTS
ABSTRACT ................................................................................................................................... iii
ACKNOWLEDGEMENTS .............................................................................................................v
DEDICATION ............................................................................................................................... vi
LIST OF TABLES ........................................................................................................................ vii
LIST OF FIGURES ..................................................................................................................... viii
INTRODUCTION ...........................................................................................................................1
Measures of Caffeine Consumption .............................................................................................1
Caffeine Expectancies ..................................................................................................................6
Impulsivity .................................................................................................................................14
Caffeine and Impulsivity ........................................................................................................20
A Model of Augmented Caffeine Consumption ........................................................................22
Impulsive Populations ............................................................................................................22
Dopamine and Adenosine.......................................................................................................25
Self-Medication Hypothesis ...................................................................................................26
Summary.................................................................................................................................27
Hypotheses .................................................................................................................................28
GENERAL METHOD ...................................................................................................................29
Overview ....................................................................................................................................29
STUDY 1 .......................................................................................................................................29
Method .......................................................................................................................................30
Participants .............................................................................................................................30 Measures .................................................................................................................................30
Procedure ................................................................................................................................33
Results ........................................................................................................................................34
Discussion ..................................................................................................................................37
STUDY 2 .......................................................................................................................................37
Method .......................................................................................................................................38
Participants .............................................................................................................................38
Measures .................................................................................................................................39
Procedure ................................................................................................................................43
Results ........................................................................................................................................44
Discussion ..................................................................................................................................55
SUMMARY AND CONCLUDING DISCUSSION .....................................................................56
REFERENCES ..............................................................................................................................62
APPENDICES ...............................................................................................................................69
iii
ABSTRACT
The aim of the current study was to examine the ability of caffeine expectancies and
impulsivity to predict caffeine consumption. The present research modified an existing caffeine
consumption instrument to parallel present day availability of caffeine containing products.
Using the modified instrument, the current research assessed if impulsivity and caffeine
expectancies would predict self-reported weekly caffeine consumption in a college sample. To
this end, two studies were conducted. Study 1 was a pilot study that assessed the usefulness of
the modified version of the self-report instrument. Results from Study 1 indicated that the
measure was useful and that only slight modifications were needed to proceed. Study 2
examined the ability of caffeine expectancies and the personality construct of impulsivity to
predict weekly caffeine consumption. Caffeine expectancies and impulsivity were measured
using relatively new instruments. The association of caffeine expectancies and impulsivity to
weekly caffeine consumption was analyzed through the construction of hierarchical multiple
regression models. Predictor variables included gender, frequency of nicotine consumption
within the last 3 months, four subscales that measured different factors of caffeine expectancies
and five subscales that measured different dimensions of impulsivity. The outcome variable was
weekly caffeine consumption. The current research hypothesized that impulsivity and
expectancy would predict college students’ caffeine consumption. Specifically, it was thought
that those endorsing higher expectancies for withdrawal symptoms would be higher caffeine
consumers. In relation to impulsivity, it was thought that sensation seeking would be the best
predictor of caffeine consumption. The hypotheses were partially supported. Results from
hierarchical multiple regression analyses performed in Study 2 indicated that caffeine
expectancies were good predictors of college students’ self-reported weekly caffeine
iv
consumption. Additionally, higher expectancies related to withdrawal symptoms were
associated with higher caffeine consumption. However, hypotheses generated in relation to
impulsivity, in general, were not supported. Impulsivity dimensions did not account for a
significant amount of variance in college students’ weekly caffeine consumption. However,
there was evidence of an interaction between gender and an impulsivity dimension measuring a
lack of the tendency to plan ahead and delaying one’s behavior.
v
ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to my mentor, Dr. Nora Noel. Without her
invaluable patience, guidance and support none of this would have been possible. Her
contributions to the field of psychology as a professor, researcher and clinical professional are
illustrated through the individuals she has touched with her wisdom. Through her dedication,
she has demonstrated an example of leadership and excellence which have influenced my life in
ways that words cannot express.
I would also like to thank the other members of my committee, Dr. Richard L. Ogle and
Dr. Len Lecci for their advice, support and suggestions. These individuals also provided an
example of excellence within the field of psychology that reinforced my convictions to strive for
continued academic achievement. I would also like to thank the many members of the
B.E.A.C.H. lab for their hours of hard work on this study.
I would like to extend my thanks to my family, especially my mother, Bonny Heaton, and
my grandparents, June and William McDaniel for supporting and encouraging me to pursue this
degree.
vi
DEDICATION
This thesis is dedicated to my mother, Bonny Heaton, and my grandparents, June and
William McDaniel. Their patience, guidance, support and unconditional love have meant more
to me than words could ever express.
vii
LIST OF TABLES
Table Page
1. Weekly Caffeine Consumption Estimates by Gender in Study 1…….…………………….35
2. Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 1….………..36
3. Weekly Caffeine Consumption Estimates by Gender in Study 2……………….………….45
4. Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 2…….……..46
5. Reliability Coefficients for the UPPS-P………….……………………………….………..47
6. Pearson Correlations with Weekly Caffeine Consumption by Gender…………….………49
7. Hierarchical Regression Models with Expectancy and Impulsivity……………….……….52
viii
LIST OF FIGURES
Figure Page
1. Gender and Premeditation Interaction………………….……….………………………….54
INTRODUCTION
A variety of consumables that are widely available to individuals in western societies,
contain caffeine. Such products include soft drinks, chocolate and many medications. Caffeine,
perhaps due to its availability and common acceptance, is the most consumed psychoactive
substance in the world (Ferré, 2008; Heinz, Kassel & Smith, 2009). Barone and Roberts (1996)
estimated the average daily intake for adults residing in the United States, using various surveys
on food consumption, to be 4 mg/kg body weight. In 2001, Shohet and Landrum estimated that
their sample of 691 college students consumed an average of 1,600 mg of caffeine weekly.
According to Barone and Roberts (1996), researchers have a piqued interest in examining
caffeine as a result of its mere presence in a wide variety of consumable plant species and,
therefore, in food products and beverages. Adding to its scientific appeal, caffeine has a
considerable history of human consumption that dates back to the Paleolithic period (Barone &
Roberts, 1996). Given the prevalence and extensive consumption of caffeine, research aimed at
further understanding its effects and associative factors is beneficial.
Measures of Caffeine Consumption
Previous research has measured caffeine consumption by asking participants to recall
their intake of particular, yet limited, caffeine vehicles through self-report (Landrum, 1992). For
example, Fillmore, Mulvihill, and Vogel-Sprott (1994) measured intake of caffeine by prompting
participants to indicate their consumption of coffee on a daily basis. More than a decade later,
the same method of measurement was used by different researchers. Oei and Hartley (2005)
asked participants to provide their daily use of coffee as a measure of caffeine consumption.
Although, caffeine intake was not central to their work, it, however, played a role in their
caffeine oriented research. That is, Oei and Hartley found it necessary to understand their
2
participants in terms of their actual level of caffeine consumption. They wanted to exclude
abstainers and those high in intake. Based on estimates from previous research, Oei and Hartley
portrayed their sample as including participants who were generally low in their caffeine
consumption. However, that description is difficult to ascertain given the only vehicle of
caffeine examined was coffee. Thus, restricted ability to accurately depict caffeine consumption
was a limitation in these studies.
Previous research has used self-report instruments that measure caffeine intake based on
the assumption that it can be represented by one or two vehicles. Further, these methods make
the assumption that the individual’s intake follows a daily pattern. The lack of an instrument that
includes the variety of vehicles that are available to caffeine consumers may account for the
variability seen in college samples in regard to caffeine consumption (Landrum, 1992; Shohet &
Landrum, 2001).
In 1992, Landrum developed a questionnaire to measure caffeine consumption in a more
precise and accurate way. It was the first attempt to provide an instrument for assessing caffeine
consumption that included a variety of caffeine containing products, and thus, complementing
the need existing in caffeine research. Landrum (1992) developed the Caffeine Consumption
Questionnaire (CCQ) which assessed the milligrams of caffeine consumed by participants on a
weekly basis. The instrument was a self-report measure completed by 116 participants.
Landrum wanted to demonstrate that the CCQ could be used to assess the general nature of
caffeine use among college students. The focus included a variety of caffeine containing
products. His original version of the CCQ (Landrum, 1992) contained the following categories:
Coffee, Tea, Cocoa, Chocolate, Soft Drinks and various Over-the-Counter Drugs. Additionally,
the measure divided the day into four categories, which included Morning, Afternoon, Evening
3
and Night. This gave participants the opportunity to record caffeine consumption based on the
time of day that they actually ingested the substance.
Landrum (1992) asserted that the questionnaire was sufficient for assessing caffeine
intake and could be used in other research that required a thorough measure of caffeine
consumption. Although his analyses did not find time of day to be a significant factor in relation
to caffeine consumption, it is possible that it served as a valuable memory cue for the
participants. Given that recall is often viewed as a limitation to self-report measures
(Williamson, 2007), it is essential to provide as many memory cues as possible to elicit more
accurate and precise self-report data. The CCQ (Landrum, 1992) was the first of its kind to
assess caffeine consumption via a variety of vehicles. Landrum considered the array of
beverages and foods that contained caffeine and were not only available, but commonly
consumed, and incorporated these products into the much needed self-report measure of caffeine
consumption.
Shohet and Landrum (2001) sought to further establish the usefulness of the CCQ
(Landrum, 1992). In addition to examining caffeine consumption in a college sample, they also
explored morning and evening personality types and whether classifying participants as such
would predict the time of day that they actually consumed caffeine. In sum, the aim of their
research was to provide increased data using an improved method of measuring caffeine intake
while attempting to further examine the relationship between times of consumption to time of
day preference. Their sample included 691 college participants who were administered the CCQ
(Landrum, 1992). Using the data obtained from these participants, the authors were able to
express the average weekly estimate of consumption as 1,600 mg. Only a portion of the sample
received the Morningness-Eveningness Questionnaire developed by Horne and Ostberg (1975)
4
that was used in Shohet and Landrum’s study to assess time of day preference. Shohet and
Landrum determined that the time of day preference held by the participants was correlated with
ingestion, such that those with evening preferences consumed more caffeine in the evening. No
significant correlation was found for morning types. The significance of evening preferences
further supports the idea that dividing consumption into time of day may provide further cues for
participants as they consider their regular caffeine intake. Shohet and Landrum argued for the
usefulness of the CCQ (Landrum, 1992) especially in college settings due to the development of
the instrument using such a sample, and suggesting that its continued use would encourage
uniformity within the field.
The utility of the CCQ (Landrum, 1992) is manifest by its appearance in various studies
engaging in research specific to caffeine. Since its development, other investigations have made
use of the questionnaire. For example, Jones and Lejuez (2005) examined various personality
correlates with caffeine dependence. They needed a measure to assess caffeine consumption and
used the CCQ (Landrum, 1992) to determine which participants were caffeine consumers and
which were abstainers. As recent as 2009, Heinz, Kassel and Smith have used a modified
version of the CCQ (Landrum, 1992). Their research was centered on developing a caffeine
expectancy questionnaire, and in order to do so, they required a measure of caffeine
consumption. Although brief and modified, the use of the CCQ (Landrum, 1992) in 2009
demonstrates the continued need of a measure to estimate participants’ caffeine intake in caffeine
oriented research. Landrum (1992) has provided easy access to a relatively accurate and precise
measure of caffeine intake.
Since Landrum (1992) embarked on the endeavor to develop a more precise self-report
measure that would assess weekly caffeine consumption, the availability of caffeine has changed.
5
Nearly two decades later, the current generation witnessed an explosion in products that contain
high concentrations of caffeine, specifically energy drinks (Reissig, Strain & Griffiths, 2009).
These potent vehicles are readily accessible to the everyday person, especially in the United
States (Reissig et al., 2009). The increase in these products has been large enough for members
of the scientific community to begin to question the availability of large amounts of caffeine.
Reissig, Strain and Griffiths (2009) propose that increased caffeine content, as is customary
among energy drinks, has become an issue that society needs to address. They argue that
caffeine intoxication is especially problematic among young male individuals as these beverages
are often targeted to this group. Arguments like these demonstrate not only the increased
prevalence of caffeine and its consumption but that the need for measuring caffeine consumption
exists, especially among young persons. Landrum (1992) began the task of the development of
the CCQ by using college students as his sample. Further, Landrum (1992) and, subsequently,
Shohet and Landrum (2001), demonstrated the importance of measuring caffeine using a
consistent measure. The development of the CCQ (Landrum, 1992) provides a pathway for the
continued assessment of caffeine intake using a self-report measure.
Despite the fact that Landrum’s (1992) CCQ was found to be useful in previous studies,
the current research seeks to further develop the questionnaire. The need exists to modify the
questionnaire so that it accurately depicts the vehicles of caffeine consumption that are relevant
to the present day. To meet this end, the current study modified the CCQ (Landrum, 1992) by
adding categories for Ready to Drink Coffees, Energy Drinks and Energy Shots. Additionally,
the categories proposed by Landrum (1992) were expanded by adding new products to better
represent the wide range of availability. In essence, the present research attempted to provide an
extensive list of potential caffeine vehicles and their quantities in milligrams. During this
6
process, it became apparent that participants sometimes indicated caffeine containing products
that were not presented on the modified version of the CCQ. Thus, the final modified version
included additional room to write in products not listed, and if the caffeine content was available,
these products were included in the weekly caffeine consumption estimates. The final revised
version of the CCQ was divided into two sections. One section referenced a typical weekday
and the other section referenced a typical weekend day. They were identical in nature except for
asking the participant to answer according to their caffeine consumption as related to day of the
week. Together these measures assessed a typical weeks’ consumption by the participant.
The breakdown of the modified version of the CCQ was based on the assumption that
caffeine intake varies from weekdays to weekend, especially for college students. The basis of
this assumption was that the sample came from a pool of college students who had different
schedules throughout the week as compared to the weekend. It was also thought that the division
of the modified version of the CCQ would allow for memory cues on behalf of the participant
and, thus, provide a picture of a typical weeks’ consumption.
Caffeine Expectancies
Research on drug expectancies has become increasingly important as more insight has
been gained in regard to the influence of expectancies on the drug experience. In a review by
Brown (1993), drug expectancies were generally described as evolving from some type of
experience with a specific drug. Brown explains that this experience can be a result of two
different encounters with the drug in question. The first experience can be an actual interaction
and ingestion of the specific drug. The second can be from additional sources, such as other
individuals or information presented in some sort of media. That is, drug expectancies are what
a person predicts and believes will happen once consumption of a specific substance occurs.
7
They can be conceptualized as cognitive representations that can be triggered by external or
internal drug-related stimuli. According to Brown, alcohol has been the topic of interest in the
vast majority of research on drug expectancies. In comparison to alcohol, realistically little has
been studied in regard to the relationship between caffeine and expectancies. However, as with
other drugs, expectancies are assumed to play a role in the experience of the acute effects of
caffeine use as well (Heinz, Kassel & Smith, 2009). Since an individual’s predictions and beliefs
can influence their subsequent experience of a drug, it is important to examine these expectations
and understand of what exactly these expectations consist. The limited research examining
caffeine expectancies illuminates the increasingly apparent need to study this relationship.
Fillmore and Vogel-Sprott (1992) conducted some of the first scientific endeavors
focusing on caffeine expectancies. Their earliest research attempted to manipulate the effects
that participants expected caffeine to produce on a task that involved the use of their motor skills.
The task employed by Fillmore and Vogel-Sprott (1992) was a computerized pursuit rotor task.
Their experiment was divided into two studies that, when combined, used 56 male participants.
Through random assignment, the authors produced four groups of individuals for each study.
Group status was defined by the attempted manipulation of the expected acute effects of caffeine
and their belief as to whether they had ingested the substance. Three of the groups anticipated
administration of caffeine via the vehicle of coffee, however, received only minute amounts of
caffeine in their beverage of decaffeinated coffee. The fourth group, which served as a control,
neither expected nor received caffeine. Thus, three of the four groups acted as placebos. That is,
none of the groups received a significant amount of caffeine in their beverage. Of the three
placebo groups, the attempted manipulation for two of the conditions included statements made
by research assistants as to the expected effects of caffeine on motor performance. Specifically,
8
one group was told that caffeine enhances motor performance; whereas a second group was
instructed to believe the opposite (i.e., diminishes performance). Thus, an attempt at
manipulation of participant’s expectancies was made by telling the volunteers what to anticipate
from caffeine administration. Research assistants made no statements concerning expected
performance to those in the third placebo group.
Fillmore and Vogel-Sprott (1992) found that what they told participants seemed to affect
participants’ performance on the computerized pursuit rotor task. Those who were in the
impairment condition demonstrated diminished functioning when compared to those in the
placebo group that were not subject to the research assistant’s statements. The same was true for
those who were in the enhanced functioning condition. These participants exhibited significantly
better scores on the pursuit rotor task than those in the placebo group who did not experience the
attempted manipulation. Thus, the research by Fillmore and Vogel-Sprott (1992) attempted to
demonstrate that caffeine expectancies could be manipulated and that this manipulation would
affect performance even under placebo conditions. However, these conclusions are difficult to
draw due to the lack of a measure of preconceived expectancies.
In an effort to extend the research on attempted manipulation of drug expectancies,
Fillmore, Mulvihill and Vogel-Sprott (1994) examined alcohol as well as caffeine. As was
previously done (Fillmore and Vogel-Sprott, 1992), Fillmore et al. (1994) attempted to
manipulate drug expectancies by having research assistants inform participants to expect either
improvement or impairment on a computerized pursuit rotor task. Members of their sample,
which included 50 male participants, were randomly assigned to one of five groups, two of
which expected caffeine administration. Within the caffeine conditions, an attempt was made to
manipulate expectancies such that one group was told to anticipate enhanced performance while
9
the other was instructed to expect impairment. Similarly, two of the groups anticipated alcohol
administration. Likewise, they were split by those who were informed to anticipate enhancement
and those informed to impairment. However, none of the four groups actually received any
significant amount of drug. Ultimately, their research used four placebo conditions. The fifth
group was a control that did not expect any drug administration.
The analyses performed by Fillmore et al. (1994) yielded similar results to those found in
Fillmore and Vogel-Sprott (1992). That is, there was an interaction between the drug and
expectancies, such that those who anticipated increased performance demonstrated such when
compared to their respective impairment expecting counterparts. Further, the researchers found
that those in the alcohol condition that expected impairment actually demonstrated increased
performance when compared to those in the caffeine condition that also anticipated impairment.
The authors predicted this relation and proposed that motivation to counteract expected
impairment, especially to alcohol, may be the responsible factor underlying these results. The
authors had no measure to assess preconceived predictions and beliefs in regards to caffeine
consumption. Therefore, their attempted use of expectancy as a manipulation provided much
insight in the research on caffeine during that time.
More than a decade later, other research has continued to attempt to manipulate caffeine
expectancies. A study by Harrell and Juliano (2009) examined how caffeine expectancies can
influence performance tasks as well as subjective reports using a 2 x 2 design. Their sample
included 60 coffee drinkers. As was previously done (Fillmore and Vogel-Sprott, 1992; Fillmore
et al., 1994), Harrell and Juliano attempted to manipulate caffeine expectancies by telling
participants to anticipate either improvements or impairment in functioning. However, their
focus included actual administration as opposed to limiting their scope to only the exploration of
10
the placebo effect. Participants were either given decaffeinated coffee with tonic water or
decaffeinated coffee with 280mg of added caffeine. They were told that they had been
administered caffeine regardless of actual caffeine content. The performance tasks that the
participants completed included a finger tapping task (Heatherly, Hayward, Seers & Rogers,
2005) and the Rapid Visual Information Processing Task (RVIP; Yeomans, Ripley, Davies,
Rusted, & Rogers, 2002). The participants were familiar with both measures due to a previous
practice session used to establish baseline measurements.
Harrell and Juliano (2009) found that administration of caffeine did have an effect on
performance. Relative to placebo, the caffeine condition produced enhancement on both
performance tasks. Further, an interaction between dose and expectancy was found only relative
to performance on the RVIP (Yeomans et al., 2002). Subjects who expected impairment and did
not receive caffeine actually improved performance compared to those who expected
enhancement. This finding was contrary to their predictions, and the authors suggested a
compensatory responding theory previously offered by Fillmore et al. (1994) as an explanation.
That is, since the subjects expected impaired functioning due to caffeine intake, they had
prepared themselves to counteract for that impairment, thus, increasing their performance when
no drug was administered. Despite that the compensatory responding theory seems to explain
the results obtained by Harrell and Juliano, it is interesting to note that Fillmore et al. found this
relation with alcohol and not with caffeine. Harrell and Juliano also found significant results in
regards to expectancy and subjective reports. They found that those who expected caffeine to
impair their functioning and were administered caffeine reported more negative somatic effects
than those who expected enhanced functioning.
11
Research that has attempted to manipulate caffeine expectancies has been popular and
has provided interesting results. However, in the absence of measuring preconceived
expectancies, it is difficult to ascertain that they have been, in fact, manipulated. It is possible
that people anticipate different effects based on the vehicle of consumption presented.
Therefore, the need exists to understand what people expect from caffeine itself, before an
attempt can be made to manipulate caffeine expectancies. The literature has been lacking of an
instrument to measure these preconceived beliefs. Despite the absence of such a measure,
Fillmore and Vogel-Sprott (1994) began to explore the idea of measuring caffeine expectancies.
The researchers did not attempt manipulation of drug expectancies; instead, they made an effort
to measure participants’ expectancies for either caffeine or alcohol depending upon group status.
The researchers randomly assigned 40 male participants to one of five groups. The groups
included both caffeine and alcohol administration, in addition to placebos for both substances.
There was also a control group that received no drug. Participants completed a computerized
pursuit rotor task as a measurement of performance. Expectancy measurements were not
obtained through examination of various factors pertaining to the construct. Rather, the variable
of expectancy was expressed as ultimately either enhance or impair. The scoring was based on a
13-point likert scale that allowed for severity of expectation, thus creating a continuum.
Fillmore and Vogel-Sprott (1994) found a positive relationship between the drug
administered and their respective expectancies. That is, the individuals that anticipated more
negative effects demonstrated diminished performance on the computerized pursuit rotor task
while under the influence of either caffeine or alcohol. Additionally, a similar relationship was
found for those participants in the placebo groups. That is, their preconceived beliefs, although
simplified due to the measure, predicted actual performance, so that if the individual expected a
12
deficiency in functioning, diminishment was actually seen in their subsequent scores on the
computerized pursuit rotor task. Their results demonstrated the effect that caffeine expectancies
can have on behavior in individuals. Further, it portrayed that there are individual differences in
caffeine expectancies.
Past research has attempted to manipulate caffeine expectancies, but none had attempted
to directly measure them as has been done in the large amount of research conducted with
alcohol (Heinz, Kassel & Smith, 2009). That is, a dimensional exploration of the construct of
caffeine expectancy had not been undertaken. The deficiency of such studies has been due to the
lack of a measurement questionnaire designed to assess caffeine expectancy. However, Heinz,
Kassel, and Smith (2009) have developed a measure that they call the Caffeine Expectancy
Questionnaire. The focus of their research was to develop the Caffeine Expectancy
Questionnaire using an alternative approach to traditional measurement analyses.
Heinz et al. (2009) undertook the making of the Caffeine Expectancy Questionnaire using
what is known as the Rasch model (Rasch, 1960) to determine the important factors contributing
to caffeine expectancy. Their study consisted of five stages, whose succession led to the final
version of the questionnaire. The authors used focus groups in the first phase to determine the
participants’ perceptions of caffeine and their expectancies upon use. The second stage included
using an actual version of the Caffeine Expectancy Questionnaire developed from the responses
given during the focus groups. This form of the Caffeine Expectancy Questionnaire consisted of
47 items and a 10- point likert scale for scoring, all of which were piloted during this stage.
Further, using the Rasch model (Rasch, 1960), Heinz et al. determined which items were
appropriate for use and found the scoring, although representing a continuum, to be insufficient.
The third stage included using a revision of the Caffeine Expectancy Questionnaire based on the
13
items that were deemed appropriate according to the Rasch model (Rasch, 1960). This version
contained 40 items. Further, the authors explored scoring of the Caffeine Expectancy
Questionnaire during this stage such that scoring would be ultimately dichotomous in nature.
That is, the authors employed the use of a 4-point scale that assessed the likelihood of a
participant expecting the proposed item with no neutral component. Additionally, Heinz et al.
explored the factors that make up the construct of caffeine expectancy and found, through
exploratory factor analysis, three factors to be present. Once again, the fourth stage saw a
revision of the Caffeine Expectancy Questionnaire and yet another administration. Scoring was
on a 4-point scale that assessed the likelihood of experience. However, the questionnaire
contained only 31 items. Exploratory factor analysis revealed the presence of four factors. The
final version contained 37 items which where categorized into four factors: Positive Effects,
Acute Negative Effects, Mood Effects, and Withdrawal Symptoms. These factors were found to
be present and distinct through the use of exploratory factor analysis. Upon the final revision of
the Caffeine Expectancy Questionnaire, it was decided to have subjects answer the items in an
ultimately dichotomous fashion that ascertained whether or not the subjects agreed with the
statement as opposed to the likelihood of experiencing such factors. Similarly, the options made
up a 4-point likert scale that provided the severity of the amount of agreement or disagreement
and ranged from 1 (Strongly Disagree) to 4 (Strongly Agree). It was decided to assess degree of
agreement as opposed to likelihood of experience due to the fit of the rating scale. That is,
endorsing agreement seemed to fit items on the questionnaire and to better distinguish between
factors.
In terms of content validity, it appeared that the Caffeine Expectancy Questionnaire
(Heinz et al., 2009) did assess the construct of caffeine expectancies. Adding evidence to the
14
content validity of the measure, Heinz et al. (2009) used focus groups in the first stage of
development. Thus, they employed others’ perceptions of caffeine expectancies and then used
these perceptions to construct the Caffeine Expectancy Questionnaire. Further, each item in the
final version of the Caffeine Expectancy Questionnaire (Heinz et al., 2009) had a correlation
coefficient greater than .35, with most items exceeding .40. In terms of external validity, the
authors explored the relationship between the factors and reported withdrawal symptoms and
dependence on caffeine. They found that those who were more dependent on caffeine scored
higher on each factor than those who were not dependent. Thus, the authors concluded that
external validity was relatively high, in that the measure correlated with another instrument that
participants should score high on, especially if they were dependent upon caffeine. The authors
performed alpha reliability coefficients on the four factors and found them all to be higher than
.80. Based on these analyses, the Caffeine Expectancy Questionnaire (Heinz et al., 2009) seems
to be reliable and valid measure for assessing caffeine expectancy. Nonetheless, further
research is needed to continue to validate the measure. There were limitations within the study,
including lower rates of participation for those persons endorsing higher consumption of
caffeine. That is, Heinz et al. found that most of the participants were relatively inexperienced in
using caffeine and thus, would be likely to endorse caffeine expectancy differently.
Impulsivity
While drug specific expectancies seem to be a reasonable concept to consider when
researching any drug, another area that is often explored is personality. In alcohol research, one
personality construct that has been found to be related to college alcohol use is impulsivity or
impulsive-like traits (Magid & Colder, 2007; Schwartz, Burkhart, & Green, 1978). Specifically,
alcohol research has often cited a positive relationship between alcohol use and sensation
15
seeking (Schwartz et al., 1978). Regarding the relationship between caffeine consumption and
personality traits, the field is lacking in availability of research. There have been a limited
number of studies that have examined caffeine consumption, caffeine dependence and other
caffeine-related variables and their relationship to impulsivity or impulsive-like traits. The
evidence generated from this body of research contains mixed results. The lack of evidence for a
robust association may be due to the variety of instruments that are used to assess impulsivity or
impulsive-like traits.
The term impulsivity is often used colloquially with the implication that its meaning is
not only understood but clear and un-muddled. The same conundrum appears to plague the
academic literature. However, researchers have begun to highlight that the use of this term gives
rise to a multitude of definitions, and there are many methods for its assessment (Evenden, 1999;
Lynam & Miller, 2004). In fact, it appears that the sole consensus in relation to the construct is
that it is multi-dimensional (Evenden, 1999), and this, in and of itself, seems to stem from the
variety of measures that are thought to assess impulsivity or tap into related factors. Evenden
(1999) states that “. . . there is not one unitary ‘impulsivity’ or only one type of impulsive
behaviour. Instead, there are several related phenomena which are usually classified together as
impulsivity. . .” (p. 348). Despite this commonality, the differences in operationally defining the
construct become evident when the varieties of instruments that are used to assess impulsivity
are considered. This observation, alone, demonstrates the lack of consensus within the
personality literature as to the nature of this seemingly elusive construct (Evenden, 1999; Lynam
& Miller, 2004).
Not only are there a variety of instruments, but each instrument seems to contain different
subscales that are thought to tap into different dimensions of impulsivity, which elucidates its
16
multi-dimensional nature. For example, the variation in impulsivity can be seen in that in some
instances impulsivity is considered a first order construct (e.g., the UPPS-P; Lynam, Smith,
Whiteside, & Cyders, 2006) while in other instances it is a second or third order construct (e.g.,
the Temperament and Character Inventory; TCI-125; Cloninger, Przybeck, Svrakic & Wetzel,
1994). It appears that impulsivity is elusive, and that even when it is observed solely as a
personality construct, it is difficult to ascertain an operational definition, much less its factors, an
idea elaborated on by Smith, et al. (2007). Smith, et al. went so far as to say that the term
impulsivity should not be used. However, based on the research, the construct of impulsivity
appears to exist. Researchers should be weary of the ways in which they report their findings
regarding impulsivity. Being specific as to the personality dimension or behavior they are
attempting to define should help to alleviate any confusion.
An instrument that is often cited in the personality literature as measuring impulsivity is
the Eysenck Impulsiveness Questionnaire (I7; Eysenck, Pearson, Easting, & Allsopp, 1985).
The I7 is based on Eysenck & Eysenck’s (1975) three-factor conceptualization of personality.
The three proposed dimensions include Extraversion-Introversion, Neuroticism-Stability and
Psychoticism-Conformity. The I7 (Eysenck et al., 1985) is composed of three factors:
Impulsiveness, Venturesomeness and Empathy. However, only two of the three factors are
thought to tap into the construct of impulsivity, namely, Impulsiveness and Venturesomeness.
Items assessing empathy were added to act as a buffer due to the concern that a greater degree of
diversity would be needed because items assessing Impulsiveness and Venturesomeness may
have been too seemingly related. These two factors may appear to be alike, but S.B.G. Eysenck
(1993) asserts that they are in fact different:
Our concept of Imp[ulsiveness] and Vent[uresomeness] can best be described by
17
analogy to a driver who steers his car around a blind bend on the wrong side of the road.
The driver who scores high on Imp never considers the danger he might be exposing
himself to and is genuinely surprised when an accident occurs. The driver who scores
high on Vent, on the other hand, considers the position carefully and decides consciously
to take the risk, hoping no doubt for the ‘thrill’ of the sensation-seeking arousal caused by
what he hopes will be merely a ‘near miss (p. 144).
According to S.B.G. Eysenck’s (1993) description, the Venturesomeness factor appears to
contain elements of risk-taking whereas the Impulsiveness factor involves acting without
considering the consequences. According to S. B. G. Eysenck (1993), the Venturesomeness
factor maps onto the extraversion dimension of Eysenck & Eysenck’s (1975) conceptualization
of personality, while Impulsiveness maps onto the Neuroticism dimension.
Another, recently constructed, personality measure of impulsivity is the UPPS Behavior
Scale (Whiteside & Lynam, 2001). With the aim of developing an inclusive instrument,
Whiteside and Lynam (2001) combed the available personality literature and chose impulsivity-
related self-report measures that were well known and often cited. The researchers performed
exploratory factor analyses to produce a new measure of impulsivity. Whiteside and Lynam
argued that the UPPS highlighted the multi-dimensional nature of impulsivity by allowing for
multiple “pathways to impulsive behavior” (see also Lynam & Miller, 2004).
Whiteside and Lynam (2001) grounded their analysis of impulsivity in the Five Factor
Model of personality (McCrae & Costa, 1990). The researchers used four impulsivity-related
subscales from the NEO-PI-R (McCrae & Costa, 1990), a measure that is used to assess the Five
Factor Model of personality, to begin to construct a multi-factorial conceptualization of the ways
in which an individual can come to behave impulsively (Lynam & Miller, 2004; Whiteside &
18
Lynam, 2001). The five factors of personality as proposed by the Five Factor Model include
Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness (McCrae & Costa,
1990). Whiteside and Lynam argued that the four traits that map onto three of the five
overarching personality factors provide the aforementioned pathways to impulsive behavior
(Lynam & Miller, 2004; Whiteside & Lynam, 2001). The four pathways include Impulsiveness,
Self-Discipline, Excitement Seeking and Deliberation (Lynam & Miller, 2004; Whiteside &
Lynam, 2001).
Whiteside and Lynam (2001) administered a variety of self-report measures that
considered impulsivity to be a first-order construct or that contained subscales thought to tap into
impulsive-like traits to 437 college students. Items from eight instruments were administered
and included the EASI-III (Buss & Plomin, 1975), BIS-11 (Patton, Stanford & Barratt, 1995),
the I7 (Eysenck, Pearson, Easting & Allsopp, 1985), the PRF (Jackson, 1984), the MPQ
(Tellegen, 1982), the TCI (Cloninger, Przybeck & Svrakic, 1991), the SSS (Zuckerman, 1994)
and the impulsivity scales proposed by Dickman (1990). It should be noted that when
appropriate, only the scales that were thought to tap into a dimension of impulsivity were used.
Thus, in many cases, portions of a measure were not incorporated in Whiteside and Lynam’s
work.
Whiteside and Lynam (2001) performed an exploratory factor analysis that generated a
four factor model of impulsivity. This four factor model accounted for 66% of the variance (see
also Lynam & Miller, 2004). The four factors mapped onto the four traits from the NEO-PI-R
(McCrae & Costa, 1990) and were renamed Urgency, Premeditation, Perseverance and Sensation
Seeking. Urgency, derived from the Impulsiveness trait is associated with rash actions while
experiencing a negative mood. It should be noted that in relation to Premeditation and
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Perseverance, these scales imply a lack of these traits due to the overarching construct they are
thought to be measuring, and they are derived from the Deliberation and Self-Discipline traits,
respectively. Sensation Seeking is the tendency to seek out new or novel situations and
experiences and is derived from the Excitement Seeking trait. The resulting measure was the
UPPS Behavior Scale that uses 45 items to assess the variety of ways in which an individual can
come to behave impulsively (Whiteside & Lynam, 2001; see also Lynam & Miller, 2004).
After the development of the UPPS (Whiteside & Lynam, 2001), Cyders et al. (2007)
explored the possibility of a fifth factor that could lead to impulsive behavior, Positive Urgency.
The proposed factor did not originate from any of the NEO-PI-R (McCrae & Costa, 1990)
subscales. The notion of the fifth factor came from accumulating research that positive mood
may increase the likelihood to engage in risky behaviors for some individuals. On the surface
level, the proposed factor of Positive Urgency appears to be strikingly similar to that of, what is
now termed, Negative Urgency which is the tendency to engage in risky behavior as a result of a
negative mood or state. In order to determine if a Positive Urgency factor would indeed be
useful to the conceptualization of impulsivity, Cyders et al. first constructed a Positive Urgency
subscale, originally termed the Positive Urgency Measure (PUM), to assess the proposed trait,
which resulted in a 14 item subscale. Cyders et al. then tested it alongside the UPPS dimensions
(Whiteside & Lynam, 2001) using exploratory factor analysis to determine whether the
hypothesized five factors would emerge. Exploratory factor analysis confirmed the presence of
five separate factors (Cyders et al., 2007). Additionally, using hierarchical regression analyses,
Cyders et al. found that the Positive Urgency factor explained significantly more variance in a
college sample’s risky behavior than the four factors from the UPPS (Whiteside & Lynam, 2001)
alone. Their research supports the inclusion of this scale as a measure of impulsivity. The
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culmination of the UPPS (Whiteside & Lynam, 2001) and the Positive Urgency subscale created
what is now known as the UPPS-P (Lynam, Smith, Whiteside & Cyders, 2006).
Caffeine and Impulsivity
A study by Jones and Lejuez (2005) compared impulsivity, sensation seeking and risk
taking with caffeine use and dependence. Their sample included 60 college students who were
prescreened to exclude abstainers. Based on their level of consumption and dependence, each
participant was placed into one of two groups. One division included those who were considered
high consumers. These students met criteria for dependence, modified for caffeine. The other
participants were considered low consumers. These persons did not meet criteria for
dependence. Each group consisted of 30 students, 15 of each gender. The screening process was
used in participant selection to obtain the group makeup. Self-report measures were used to
assess impulsivity and sensation seeking. These measures included a shortened version of the
Sensation Seeking Scale (Zuckerman, Eysenck, & Eysenck, 1978) and the Eysenck
Impulsiveness Questionnaire (I7; Eysenck, Pearson, Easting, & Allsopp, 1985). Risk taking was
measured behaviorally using the BART (Lejuez et al., 2002).
According to Jones and Lejuez (2005), the two groups did not significantly differ in
terms of their risk taking. Nonetheless, their initial investigation revealed that impulsivity and
sensation seeking were related to caffeine consumption and dependence. That is, those scoring
higher on both dependence and consumption also scored higher on the scales of impulsivity and
sensation seeking. The experimenters then entered the two variables into a logistic regression.
This analysis revealed sensation seeking to be the only significant factor. Although, Jones and
Lejuez reported that there was evidence of multicolinearity in that sensation seeking and
impulsivity were highly correlated. When multicolinearity is present in a regression model, the
21
resulting coefficients are not readily interpretable. The unclear association between caffeine
consumption and impulsivity as exemplified by the results reported by Jones and Lejuez
highlight the importance of examining these variables using factors that are only moderately
correlated.
Other research has sought to explore personality in relation to caffeine use while being
keenly aware of smoking’s possible interacting effects. In a study by Gurpegui et al. (2007), one
objective was to investigate the relationship between caffeine and personality. The authors
statistically controlled for smoking. According to Gurpegui et al., previous research has shown
smoking to be associated to various personality factors as well as caffeine intake. The authors
argue that due to these findings it is necessary to control for smoking behavior in order to
examine the effects of personality on caffeine use. The experimenters recruited 498 individuals
as participants in their study. Their sample included individuals from the city of Granada, Spain.
The average age of their participants was 45.1 years. Thus, the results obtained from their
research may be difficult to generalize to other populations (i.e., college students). Gurpegui et
al. used the Temperament and Character Inventory (TCI-125; Cloninger, et al., 1994) as a
measure of personality. They assessed, via self-report, demographic information, consumption
of caffeine and smoking behavior.
Gurpegui et al. (2007) did not find a significant relation between personality and general
caffeine intake. However, they did conclude that high consumption of caffeine was associated
with novelty seeking. The subscales of novelty seeking were further analyzed to determine that
only impulsivity correlated with high caffeine consumption. Their research has demonstrated
that caffeine use may be related to personality, especially for those who are high consumers.
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A Model of Augmented Caffeine Consumption
The current research proposes a model of increased caffeine consumption that is
hypothesized to be a result of high trait impulsivity. This model is based on literature concerning
impulsive populations and dopaminergic differences in these populations. The proposed model
hypothesizes that those higher on impulsivity dimensions will be augmented caffeine consumers.
The basic premise is that there may be a dysfunction in dopaminergic functioning in those
individuals who score high on measures assessing trait impulsivity and through consumption of
large amounts of caffeine, in an attempt to self-medicate, some of the impulsive symptomology
would be alleviated through indirect increases in dopamine by inhibitory adenosine receptor
binding.
Impulsive Populations
In his review, Steinberg (2008) discusses the general consensus within the literature that
adolescents and young adults seem to be more likely to engage in risky behaviors that may result
in harm either to the self or others. Steinberg (2008) proposes that the differential development
of two interacting systems in the brain may explain why risky behavior is more prevalent during
adolescence (see also Steinberg, 2010). In Steinberg’s (2010) dual systems model, the central
tenet is that there are two systems within the adolescent brain that are developing at different
rates and along different timelines. One such system that Steinberg (2008) terms the “socio-
emotional” system is linked to reward-seeking and consists of structures such as the amygdala,
ventral striatum, and medial prefrontal cortex. During adolescence, dopamine activity changes
drastically, and it is these changes that are thought to influence the increases in reward-seeking
behavior observed during adolescence (Steinberg, 2008; Steinberg, 2010). Another system that
Steinberg (2008) terms the “cognitive control” system is also implicated in adolescence risk
23
taking (see also Steinberg, 2010). This system is associated primarily with the prefrontal cortex
and is responsible for impulse control. During adolescence, this system is relatively immature
relative to the socio-emotional system. One of Steinberg’s (2010) hypotheses is that the
development of the socio-emotional system follows a curvilinear time course, decreasing after
adolescence. However, Steinberg (2010) asserts that the cognitive control system follows a
linear pattern, with self-control and regulation increasing with age. Steinberg (2010)
hypothesizes that it is the interaction of these two systems and their degree of maturity during
adolescence that results in the prevalence of adolescent risky behavior and also explains the
subsequent decreases in risky behavior that are observed in adults.
Another model that attempts to explain adolescent risk-taking behavior was proposed by
Ernst, Pine & Hardin (2006) and is termed the triadic model. According Ernst et al. (2006), “the
passage through adolescence is characterized by typical patterns of motivated behavior, namely
risk-taking, sensation/novelty/reward seeking, and impulsivity” (p. 300). Differing from
Steinberg’s (2008; see also Steinberg, 2010) conceptualization, Ernst et al. propose that it is the
interaction of three neural systems that result in these types of behaviors observed during this
developmental time period. One system includes what the authors term the “approach (reward-
driven)” system whose structure of primary concern is the ventral striatum, specifically the
nucleus accumbens. The neurotransmitter dopamine is thought to be of particular importance in
relation to goal-directed behavior in this system. Another system is the “avoidance (harm-
avoidant)” system whose structure of primary importance is the amygdala. The third system
that the authors term the “regulatory” system includes circuits of the prefrontal cortex and is
thought to mediate the relative influence of the aforementioned approach and avoidance systems.
Ernst et al. “. . . propose that adolescence is the period during which the activity of the reward
24
system prevails over that of the avoidant system while the still immature regulatory system fails
to adaptively balance these two behavioral controllers” (2006, p. 303).
The underlying theme of these two models in relation to adolescent risk taking is that
there is an imbalance between systems that involve, especially reward and impulse control.
These models propose that it is the developmental time course and relative maturity of these
systems that results in an imbalance which contributes to the increased likelihood of adolescent
risky behavior. It is the combination of a relatively weak or underdeveloped regulatory system,
which is responsible for inhibiting responses, and an overactive reward-driven or goal oriented
dopaminergic system. Despite the disparity in relation to the number of systems involved, the
prevailing view is that dopaminergic and regulatory control systems perpetuate risky and
impulsive behaviors when imbalanced or underdeveloped.
Another population that is often considered to be impulsive is those individuals
diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). According to the Diagnostic
and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association,
2000), a diagnosis of ADHD is characterized by developmentally excessive inattention,
hyperactivity or impulsivity that persists for 6 months. There are a total of 18 criteria that are
divided between two key domains, namely, inattention and hyperactivity-impulsivity. A
diagnosis, of which there are three subtypes, is warranted only if at least six symptoms are
present in at least one of the domains (American Psychiatric Association, 2000). Although
ADHD is typically equated with pediatrics and adolescents, there is increasing evidence to
suggest that the disorder continues into adulthood. As such, increasing numbers of adults are
receiving treatment for the disorder (Advokat, 2009). Psychopharmacological treatment for
individuals diagnosed with ADHD is often stimulants, such as methylphenidate and
25
amphetamine (Advokat, 2009; Tang, Wanchoo, Swann & Dafny, 2009). These psychostimulants
increase levels of dopamine in the brain (Tang et al., 2009). Despite the plethora of research on
the topic, a consensus as to why psychostimulants alleviate symptomology is unclear. However,
given that stimulants improve functioning in these individuals and that these drugs increase
dopaminergic transmission, it is reasonable to infer that dopaminergic systems within the brain
may be dysfunctional in this population as well.
Dopamine and Adenosine
According to Lodge, Buffalari and Grace (2009), there are three proposed dopamine
systems or pathways within the brain. Two of which are thought to contribute substantially to
the addiction process and include the mesolimbic and the mesocortical dopamine systems
(Goldstein, Alia-Klein, & Volkow, 2009; Heatherton & Wagner, 2011). The mesolimbic system
has its origins in the ventral tegmental area and projects to various structures that are thought to
regulate affect including the ventral striatum, nucleus accumbens, hippocampus and the
amygdala. This system is thought to play a major role in drug use. The mesocortical system also
has its origins in the ventral tegmental area but its projections terminate in the frontal cortex.
Specifically, this system includes prefrontal cortex, orbitofrontal cortex and the anterior
cingulate. This system is thought to be implicated in higher order functioning, such as, executive
control.
Caffeine increases the availability of the neurotransmitter dopamine in those areas of the
brain where dopamine is abundant (Brunyé, Mahoney, Lieberman & Taylor, 2010). The
mechanism by which caffeine is responsible for increases in dopamine activity and other
neurotransmitter activity such as norepinephrine and glutamate is the adenosine receptors,
specifically A1 and A2A. Caffeine acts as an antagonist at these receptor cites, which ordinarily
26
(i.e., in the absence of caffeine binding) act as inhibitory agents to the production of dopamine,
norepinephrine and glutamate. It is speculated that when caffeine binds to the A1 and A2A
receptors, it inhibits these receptors and prevents adenosine from binding. This inhibitory
process leads to an increase in dopamine in dopamine concentrated areas of the brain. That is,
adenosine, when bound to A1 and A2A receptors inhibits the transmission of dopamine. By this
action, caffeine increases dopamine availability through antagonistic actions (Brunyé et al.,
2010). It is this process of binding to A1 and A2A receptors that is thought to influence the
increases in cognitive stimulation and alertness produced when caffeine is ingested (Brunyé et
al., 2010).
Self-Medication Hypothesis
One predominant model of substance dependence is the Self-Medication Hypothesis
(Khantzian, 1985). In 1985, Edward J. Khantzian outlined a model of substance dependence
whose central tenet was that predisposed individuals seek out specific drugs in order to “self-
medicate” or temporarily relieve negative affective states and/or psychopathology. Specifically,
Khantzian’s (1985) model, which he has coined the Self-Medication Hypothesis, assumes that
individuals who become dependent on specific drugs do so as a result of the pharmacological
actions of the drugs and their interplay with affective and psychopathological states. The
principal assumption of the model is that dependence on a specific substance does not occur
randomly. Rather, predisposed individuals may try a variety of substances before their “drug-of-
choice” (Wieder & Kaplan, 1969) is defined. An assumption of the Self-Medication Hypothesis
(Khantzian, 1985) is that the predisposed individual develops a preference for a specific drug due
to its pharmacological actions that are believed to relieve the individual’s disturbing and
27
uncomfortable affective and psychopathological states which perpetuate continued use and
eventual dependence.
Khantzian (2003) explains that individuals who become addicted to a particular drug do
not set out with the intention of becoming addicted. Instead, these individuals are more likely to
experiment with a variety of drugs, some of which may produce more aversive as opposed to
euphoric states as a result of the pharmacological action of the drug and its interaction with their
affective state. That is, depending on the individual’s affective state or psychopathology in
which they are attempting to medicate, certain drugs may potentiate the problem as opposed to
relieve it. Under the Self-Medication Hypothesis (Khantzian, 1985), those drugs which alleviate
psychiatric symptomology or aversive affective states would be more likely to be used with
increasing frequency and therefore, dependence on that particular drug may develop.
Despite the emphasis on psychodynamic theory, Khantzian’s (1985) principal tenet is that
an individual’s drug of choice is chosen for its pharmacological action which effectively relieves
dysphoric, uncomfortable affective states and psychopathology. According to this theory, it is
not the case that individuals seek altered realities, but that they are medicating themselves to
experience an essentially “normal” state that they feel they would not be able to obtain
otherwise.
Summary
The current research proposes a model of augmented caffeine consumption. The central
premise is that impulsive populations may exhibit a dopaminergic deficit, which, in turn, leads to
impulsive behavior. Grounded in the Self-Medication Hypothesis (Khantzian, 1985), the current
research speculates that the dysfunction in dopamine systems in the brain leads to dysphoria
which the individual attempts to relieve. Given that psychostimulants are often prescribed to
28
treat ADHD, impulsive populations may consume increased amounts of caffeine, especially
given its abundant availability in comparison to other psychostimulants.
Hypotheses
Until the endeavor undertaken by Heinz et al. (2009) no previous research had examined
caffeine expectancies using a measure that is similar to those found in the alcohol literature.
Furthermore, any research that had sought to measure caffeine expectancy did so using a
simplified self-report of the construct. That is, they assumed it to be one-dimensional. The
Caffeine Expectancy Questionnaire (Heinz et al., 2009) is a dimensional approach to measuring
caffeine expectancies. The division of caffeine expectancy into four factors is a new concept in
the field of caffeine research. It would be beneficial to use the Caffeine Expectancy
Questionnaire to examine expectancies held by participants and to further establish the
usefulness of the measure. The present research examined caffeine expectancies held by subjects
using the Caffeine Expectancy Questionnaire as developed by Heinz et al. (2009). Thus, the
current study provided additional data relative to the usefulness of the Caffeine Expectancy
Questionnaire and examined the predictive ability of caffeine expectancies in relation to caffeine
consumption. The present research hypothesized that out of the four factors of caffeine
expectancy, the Withdrawal Symptoms subscale would be the best predictor of caffeine intake.
That is, those indicating more withdrawal symptoms were thought to be more likely to be high
consumers of caffeine. This relation was expected due to the probability that those who are
addicted to caffeine will experience withdrawal symptoms on a daily basis, and therefore, will
consume more caffeine to alleviate these symptoms.
Given that there are mixed results concerning the relationship between impulsivity and
caffeine consumption, one aim of the present study was to further examine the relationship
29
between self-reported caffeine consumption and the construct of impulsivity using the UPPS-P
(Lynam et al., 2006). It was thought that measuring impulsivity using the UPPS-P (Lynam et
al., 2006) would provide a dimensional approach to the assessment of the construct. Further, the
measure has been applied in other research examining nicotine (Spillane, Smith & Kahler, 2010),
which has also been found to be related to caffeine use (Gurpegui et al., 2007). No other
research has used the UPPS-P (Lynam et al, 2006) to examine the association between caffeine
use and impulsivity. The current study hypothesized that impulsivity would be related to
caffeine consumption. Specifically, it was predicted that sensation seeking would be a good
predictor of caffeine consumption.
GENERAL METHOD
Overview
The current study was divided into two phases. Study 1 piloted a caffeine consumption
instrument. Study 2 was longitudinal and asked volunteers to return in exactly one week to
complete all measures excluding those that would provide redundant information (i.e.,
demographics and contact information under the guise of code names). The current study
measured caffeine consumption using the instrument that was piloted in Study 1 to determine the
predicative ability of caffeine expectancies and the personality construct of impulsivity.
STUDY 1
The main objective of Study 1 was to establish the usefulness of a modified version of the
Caffeine Consumption Questionnaire (Landrum, 1992), henceforth referred to as the Pilot CCQ.
In order to understand its usefulness and how to proceed for Study 2, the amount of time required
to complete the measure, its level of difficulty and its ability to represent the most common
vehicles available to persons likely to encompass the sample (i.e., college students) needed to be
30
understood. With these goals in mind, the Pilot CCQ and a series of questionnaires were
administered to students attending a southeastern university during the Spring semester of 2010.
Method
Participants
The original sample size included 100 students; however, data from three participants
were excluded due to preselected age restrictions. Analyses were conducted on data obtained
from 97 students between the ages of 18 and 30 years. Those that indicated that they were 21 or
younger made up 92.8% of the sample (M=19.35; SD=1.633). The majority of the sample,
63.9%, indicated that they were of freshmen status, 15.5% were college sophomores, 15.5%
were juniors, and the remaining 5.2% were seniors. Most of the sample was females,
representing 72.2% (n = 69). Males made up the other 27.8% of participants (n = 25).
Participants describing themselves as White or Caucasian made up 81.4% of the sample. Those
indicating that they were of African American decent included 4.1% while 3.1% specified that
they were of Asian/Pacific Islander decent. Most students who volunteered were enrolled in an
Introductory Psychology course. Participation was completely voluntary. Each person received
one research credit as compensation.
Measures
In order to better describe the sample, an instrument that would measure participants’
characteristics was needed. To meet this need, a demographics questionnaire that asked
participants to provide their gender, age, height, weight, ethnicity, educational level and marital
status was developed. Additionally, the current research asked participants to describe their
physical health as either poor, fair or excellent. Participants’ frequency of use of other
substances was also examined which included the use of non-prescribed drugs (16 items) and
31
prescribed substances (10 items) over the past three months using a 7-point likert scale that
ranged from 0 (Never) to 6 (Almost Every day). The format used for assessing other substance
use was derived from the Quantity Frequency Index that has been commonly used in alcohol-
related research (Cahalan, Cisin & Crossley, 1969).
To measure caffeine consumption, the current study developed the Pilot CCQ. The
modified version of Landrum’s (1992) measure consisted of an extensive list of caffeine
containing products. All of the original categories were incorporated, including Coffee, Tea,
Coca, Chocolate Milk, Soft Drinks, Over-the-Counter Drugs and Food. To exemplify the most
commonly available size of a product in question, serving sizes were increased for every
classification except Over-the-Counter Drugs. To better represent the diversity of caffeinated
goods, items were either added or changed to five of the divisions including Coffee, Tea, Soft
Drinks, Over-the-Counter Drugs and Chocolate. Three categories were added: Ready to Drink
Coffee, Energy Drinks and Energy Shots. The Pilot CCQ assessed participants’ consumption of
a total of 89 items.
Mimicking Landrum’s (1992) work, the Pilot CCQ allowed participants to indicate what
time of day they actually consumed various caffeinated products. The hours of the day were
grouped into four sections, including Morning, Afternoon, Evening and Night. In order to
provide additional memory cues, the Pilot CCQ was separated into two parts. This division
differed from the format of Landrum’s version. The first section, Monday-Friday, asked
participants to indicate how many servings of each caffeinated product that they consumed
during the typical weekday. The second portion, Saturday-Sunday, examined the consumption
of caffeine on the typical weekend day. Each division contained identical categories and items to
assess caffeine intake by participants. The difference between the two sections was the part of
32
the week that the participants were asked to consider for their answers. The Pilot CCQ provided
two average estimates of consumption. The weekday estimate was multiplied by 5, and the
weekend day estimate was multiplied by 2. These products, added together, created an average
weekly consumption estimate, in milligrams, for any given participant.
Throughout the measure, participants were provided with room to write suggestions,
comments and items they would add. The end of a section was denoted with instructions and
encouragement to provide feedback regarding the research. Following the end of the Monday-
Friday section of the Pilot CCQ, another segment of the measure addressed various caffeine
related topics. Participants were asked to indicate any times during which they abstained from
using the substance. Further, they described themselves in terms of their level of consumption
and whether they should lower, increase or do nothing about it. This section also prompted
participants to indicate any substances that they used in combination with caffeine. Withdrawal
symptoms and caffeine intoxication were also assessed.
Caffeine expectancies were measured with a relatively new self-report instrument.
Using exploratory factor analysis, Heinz, Kassel and Smith (2009) developed the first
multidimensional Caffeine Expectancy Questionnaire. The use of the Caffeine Expectancy
Questionnaire allowed for analysis of four factors and their relation to caffeine consumption.
These factors included Positive Effects, Acute Negative Effects, Withdrawal Symptoms and
Mood Effects. The Positive Effects and Withdrawal Symptoms scales were measured using 11
items each. The Acute Negative Effects scale contained eight items. Mood Effects were
measured with a total of seven items. Items were rated using a 4-point likert scale ranging from
1 (Strongly Disagree) to 4 (Strongly Agree). The original version of the Caffeine Expectancy
Questionnaire was administered to all participants; however, three items were added. Two of the
33
additional items assessed the expected use of caffeine during a woman’s premenstrual cycle and
throughout the entire menstrual period. The third additional item assessed the expectation held
by participants for caffeine to sober them up.
Procedure
In Study 1, the demographics questionnaire, the Pilot CCQ and the Caffeine Expectancy
Questionnaire (Heinz et al., 2009) were administered. The measures were completed in groups
of no larger than five participants. The room contained several seating options, including, two
sofas, two student desks and one recliner. There was a one-way mirror that was used to
randomly monitor the two research assistants as they administered the study. Study 1 was
completely anonymous. At the beginning of each session, an experimenter read the rights of
volunteers participating in research aloud to the group before handing out the demographics
questionnaire, which contained the same statement of participants’ rights. Upon completion of
the demographics portion, the students were asked to place their form in a manila folder that was
provided for each individual. After everyone in the group had finished, the research assistant
read the instructions aloud for the next questionnaire. Administration of the Pilot CCQ and the
Caffeine Expectancy Questionnaire continued in this manner. After all measures were
completed, the participants were then asked to answer a series of questions that were presented
orally by a research assistant. These questions addressed the difficulty of the Pilot CCQ, items
that could be added or changed, and any other suggestions. The participants were asked to
contemplate each question and write down their responses. Afterwards, the research assistants
encouraged the group to discuss their answers orally while one of the experimenters recorded
their responses on a separate form.
34
Results
Descriptive statistics and further analyses were performed using PASW statistics
software version 18.0. Males were between the ages of 18 and 23 (M=19.22; SD=1.34). Their
self-reported weekly consumption of caffeine averaged at 4,101 mg. Female participants were
between the ages of 18 and 30 (M= 19.40; SD= 1.74). Females consumed, on average, 3,398 mg
of caffeine weekly. Table 1 contains means and ranges of relevant weekly caffeine consumption
estimates by gender.
Independent samples t-tests revealed that there were no significant differences between
males and females in terms of total weekly caffeine consumption, t (92) = 0.87, p = .387
Likewise, independent means t-tests revealed that there were no significant differences between
males and females in terms of weekly morning caffeine consumption, t (92) = -0.43, p = .669.
Independent means t-tests between males and females also revealed that there were no
significant differences between males and females in terms of weekly evening caffeine
consumption, t (92) = 1.68, p = .097.
Internal consistency for the subscales of the Caffeine Expectancy Questionnaire (Heinz et
al., 2009) was assessed. The reliability coefficient, Cronbach’s alpha, exceeded .80 for all four
subscales, suggesting that the items that make up each subscale appear to assess the same
construct. These results reflect the reliability coefficients reported by Heinz et al. (2009).
Means, standard deviations and reliability coefficients for the Caffeine Expectancy
Questionnaire are reported in Table 2.
35
Table 1
Weekly Caffeine Consumption Estimates by Gender in Study 1
Portion of the Week Range M SD
Malesa
Total Weekly Consumption 126 – 18,282 4,101 3,938
Weekly Morning Consumption 30 – 15,158 2,180 2,962
Weekly Evening Consumption 0 – 7,228 1,920 1,957
Femalesb
Total Weekly Consumption 0 – 14,923 3,398 3,283
Weekly Morning Consumption 0 – 36,620 2,614 4,723
Weekly Evening Consumption 0 – 9,728 1,251 1,614
Note. Minimum, Maximum and Mean values are round to the nearest milligram. a n = 25. b n = 69.
36
Table 2
Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 1
Subscale Items M SD α
Acute Negative Effects 8 15.48 4.56 .84
Positive Effects 11 25.81 5.84 .84
Withdrawal Symptoms 11 18.53 6.55 .91
Mood Effects 7 12.80 3.68 .81
Note. 1 = Strongly Disagree 2 = Disagree 3 = Agree 4 = Strongly Agree
37
Discussion
The pilot study provided qualitative information as to the appropriateness of each of the
items on the questionnaire. Participants had the opportunity to discuss changes, difficulties and
additions to the Pilot CCQ. Most individuals described the Pilot CCQ as being low in level of
difficulty. Furthermore, these discussions highlighted the appropriateness of the items contained
on the questionnaire. Most participants indicated that the items presented represented the
vehicles available for consumption. Although most individuals indicated that the questionnaire
was complete, there were instances in which other possible vehicles of consumption were
suggested. Some of the products contained caffeine and some products did not. Thus, one
change that was made to the questionnaire included the option, for each category, to write in a
product and the corresponding servings consumed. This option was added to each category and
contained the same elements as every item. Thus, additional items could be quantified if the
caffeine content was available to the public. Additionally, Study 1 also highlighted the need to
emphasize, visually, correct and incorrect methods of completing the questionnaire.
Specifically, several participants attempted to fill in zeros for all products that they did not
consume. Given that the questionnaire contains 89 items, this was a daunting task. Thus,
another change that was needed included visual examples of the completion of the questionnaire
using Microsoft PowerPoint 2007.
STUDY 2
Similar to Study 1, an objective of Study 2 was to continue to establish the usefulness of
a modified version of the CCQ (Landrum, 1992). The version used in Study 2 was referred to as
the CCQ 2010. Study 2 was longitudinal, and participants were asked to complete measures on
two occasions separated by exactly one week. Most volunteers were students enrolled in an
38
Introductory Psychology course at a southeastern university. They were asked to come to a
classroom or a lab for both portions during the Fall 2010, the Spring 2011 and the Fall 2011
semesters.
Method
Participants
The original sample size included 323 students. However, data from a total of 11
participants were omitted: three exceeded preselected age restrictions, four had extreme values
on the dependent measure that exceeded four standard deviations above the mean, and four
incorrectly completed the dependent measure and their weekly caffeine consumption estimate
could not be quantified. Analyses were conducted on data obtained from 312 participants
(96.6% of the original sample) that were between the ages of 18 and 29 years (M= 18.92; SD =
1.73). Those that indicated that they were 21 or younger made up 93.9% of the sample. Most of
the sample was males, representing 51.6% (i.e., 161 participants). Females made up the other
48.4% of the sample (i.e., 151 participants). The majority of participants, 70.2%, indicated that
they were of freshmen status, 18.6% were college sophomores, 6.4% were juniors, and 3.8%
were seniors. Participants who described themselves as White or Caucasian made up 85% of the
sample. Those indicating that they were of African American decent included 5.2% while 4.9%
specified that they were of Hispanic descent. The majority of the sample, 92.6%, returned
exactly one week after the initial questionnaire administration (i.e., Part I) for another
administration of questionnaires (i.e., Part II). Participation was entirely voluntary. As
compensation, each person received two research credits which fulfilled their need for the
semester.
39
Study 2 required protective measures to ensure that confidentiality was maintained. The
students that volunteered for Study 2 were asked to return to provide longitudinal information.
As a result, it was necessary to match the measures that were completed on the first occasion
with those completed on the second occasion for each student. To ensure confidentiality,
volunteers were asked to make up a code name that was to be used in place of their real
identification. Participants were informed that their code could not be offensive or obscene in
nature. The only form on which participants wrote their real name was the informed consent.
Each participant signed two copies of the informed consent, one of which a research assistant
kept in a separate manila envelope that was intended for the entire group, and the other the
participant kept for their own records. The participants’ code names and real names were never
matched. The students were asked to provide a telephone number that they could be reached in
the week to come. A master list that contained the code names and respective telephone
numbers was kept in a locked cabinet and was only available to the primary investigators. The
participants were forewarned to expect a call from one of the primary investigators who asked
for them by their code name to remind them of their testing appointment. The room where the
locked cabinet was located was also locked and only those with explicit permission were allowed
to enter.
Measures
Study 2 used the same demographics questionnaire that was developed for Study 1. The
instrument assessed the following participant characteristics: gender, age, height, weight,
ethnicity, education level and marital status. As in Study 1, the demographics portion asked
participants to indicate their level of physical health as either poor, fair or excellent. One
difference was the ability to indicate veteran status. Each volunteer was also asked to specify
40
their form of nicotine intake, if applicable. Another difference was that the demographics
portion no longer included frequency of use of other substances due to the full administration of
the Quantity Frequency Index (Cahalan et al., 1969) which contained this information.
The current study created a questionnaire that addressed various caffeine related topics.
Specifically, participants were asked to indicate how many milligrams of caffeine and cups of
coffee they consumed weekly. Participants were also asked to indicate any times during which
they abstained from using caffeine. Further, they described their level of consumption as to
whether they felt that it should be lowered, increased or if it was about right. This section also
prompted participants to indicate any substances that they used in combination with caffeine
specifically. Withdrawal symptoms and caffeine intoxication were also assessed using a 5-point
likert scale, ranging from 0 (Never) to 4 (Very Often). This questionnaire also included a section
specifically for females that assessed the current week in their menstrual cycle.
Caffeine consumption was measured using a modified version of the Pilot CCQ. This
version was referred to as the CCQ 2010. The measure included all 89 items presented in Study
1 and two additional items, the inclusion of which was determined by the statements and
suggestions made by participants in Study 1. The option to write the name of a possible caffeine
containing product, not listed, was added to each category to allow participants to indicate any
possible caffeine containing vehicles they may have consumed. The caffeine content of any
additional products that a particular individual indicated they consumed was added to the
participants’ consumption estimate if it was available to the general public.
The CCQ 2010 followed the same format as the pilot version in that it was divided into
two parts that assessed Monday-Friday and Saturday-Sunday consumption. However, the
separation between the two sections did not include additional questions regarding caffeine use
41
(i.e., withdrawal and intoxication symptoms). These issues were addressed using a separate
questionnaire. As in Study 1, participants were asked to complete the CCQ 2010 based on a
typical week’s consumption. Instructions for completing the instrument were written on the
form as well as presented visually using Microsoft PowerPoint 2007. Correct and incorrect
examples of completed portions of the questionnaire were provided. The visual aid also
demonstrated pictured examples of serving sizes for various products. The CCQ 2010 was used
to produce a typical week’s average estimate of caffeine consumption in milligrams.
The second administration asked participants to complete a similar version of the CCQ
2010. The entire format of the CCQ 2010 presented on the second administration was the same
except caffeine consumption was referenced to the previous week. The second administration
did not include a visual aid.
As in Study 1, the Caffeine Expectancy Questionnaire developed by Heinz et al. (2009)
was used to determine the relationship between expectancies and caffeine consumption. The
instrument allowed for analysis of four factors, which included Positive Effects, Acute Negative
Effects, Withdrawal Symptoms and Mood Effects. The Positive Effects and Withdrawal
Symptoms scales were measured using 11 items each. The Acute Negative Effects scale
contained eight items. Mood Effects were measured with a total of seven items. Items were
rated using a 4-point likert scale ranging from 1 (Strongly Disagree) to 4 (Strongly Agree). The
same modification that was made in Study 1 was used in Study 2. That is, three items were
added that addressed using caffeine during the menstrual cycle and the use of caffeine to
counteract the effects of alcohol.
The personality construct of impulsivity was measured by using the UPPS-P (Lynam et
al., 2006). This instrument allows for a dimensional approach to defining impulsivity. It is a
42
self-report measure that assesses the construct based on five subscales: Positive Urgency,
Negative Urgency, Sensation Seeking, Perseverance and Premeditation. The instrument contains
a total of 59 items which are rated using a 4-point likert scale, ranging from 1 (Extremely
Uncharacteristic of me) to 4 (Extremely Characteristic of me). Negative Urgency and Sensation
Seeking are comprised of 12 items each. The subscale, Negative Urgency, is associated with
rash actions while experiencing a negative mood. Positive Urgency relates to the tendency to
engage in reckless behavior in response to a positive mood and is assessed using 14 items.
Perseverance contains 10 items. Premeditation is measured using 11 items. All five subscales
were employed in the current research to further examine the relationship between caffeine
consumption, caffeine expectancies and impulsivity.
The Eysenck Impulsiveness Questionnaire (I7; Eysenck et al., 1985) was used as another
measure of impulsivity due to the relatively recent development of the UPPS-P (Lynam et al.,
2006). The I7 (Eysenck et al., 1985) consists of 54 dichotomous yes/ no items that make up three
subscales: Venturesomeness, Impulsiveness and Empathy. While Venturesomeness and
Impulsiveness are thought to tap into the construct of impulsivity, Empathy was included to add
variety to the questions asked (Eysenck, 1993).
The Quantity Frequency Index (Cahalan et al., 1969) was used to assess quantity and
frequency of alcohol use during the last 90 days. Frequency and amount were assessed for three
vehicles of alcohol consumption (i.e., hard liquor, wine and beer). The participants’ frequency
of other substances was also assessed. Other substances that were examined included the use of
non-prescribed drugs (i.e., 16 items) and prescribed substances (i.e., 10 items) over the past three
months using a 7-point likert scale that ranged from 0 (Never) to 6 (Almost Every day).
43
Procedure
In Study 2 several self-report questionnaires were administered which included an
informed consent form, the demographics questionnaire, the general caffeine use questionnaire,
the Caffeine Expectancy Questionnaire (Heinz et al., 2009), the CCQ 2010, the I7 (Eysenck et
al., 1985), the UPPS-P (Lynam et al., 2006), and the Quantity Frequency Index (Cahalan et al.,
1969). Participants were tested in groups no larger than 20 in a classroom or in groups no larger
than seven in a laboratory setting. Participants in Study 2 were asked to physically attend two
sessions that were separated by exactly one week. Study 2 was confidential and required that the
students receive and sign an informed consent form which explained their rights as a research
participant and the confidential nature of the study. To aid in further understanding and accuracy
when completing each measure, the presentation of the instructions for all measures during Part I
was both oral and visual. Microsoft PowerPoint 2007 was used as a visual aid. Upon
completion of each questionnaire, the students were asked to place their form in a manila folder
that was provided for each individual. After everyone in the group had finished, the research
assistant read the instructions aloud for the next questionnaire. Administration of all measures
continued in this manner. Part II required participants to complete several measures for a second
time, including a general caffeine use questionnaire, the Caffeine Expectancy Questionnaire
(Heinz et al., 2009), the CCQ 2010, the I7 (Eysenck et al., 1985), the UPPS-P (Lynam et al.,
2006), and the Quantity Frequency Index (Cahalan et al., 1969). Directions for the general
caffeine use questionnaire and the CCQ 2010 explicitly referenced the previous week. All other
directions were the same, with the exclusion of the visual aid. Questionnaires given in Part II
were administered as a packet that participants were to give to the research assistant when
complete.
44
Results
Descriptive statistics and further analyses were performed using PASW statistics
software version 18.0. The current study assessed mean age and caffeine consumption by
gender. Males were between the ages of 18 and 29 (M=19.04; SD=1.94). Their self-reported
weekly consumption of caffeine averaged 3,111 mg. Female participants were between the ages
of 18 and 27 (M= 18.79; SD= 1.47). Their self-reported weekly consumption of caffeine
averaged 2,990 mg. Table 3 contains means and ranges of relevant weekly caffeine consumption
estimates by gender.
Independent samples t-tests revealed that there were no significant differences between
males and females in terms of total weekly caffeine consumption, t (310) = 0.36, p = .718.
Likewise, independent means t-tests revealed that there were no significant differences between
males and females in terms of weekly morning or evening caffeine consumption, t (310) = -0.24,
p = .810 and t (310) = 1.09, p = .278, respectively.
Internal consistency for the subscales of the Caffeine Expectancy Questionnaire (Heinz et
al., 2009) and the UPPS-P (Lynam et al., 2006) were assessed using the reliability coefficient
Cronbach’s alpha. Reliability coefficients for all four subscales of the Caffeine Expectancy
Questionnaire and all five subscales of the UPPS-P met or exceeded .80, suggesting that the
items that make up each subscale appear to assess the same construct. Means, standard
deviations and reliability coefficients for the Caffeine Expectancy Questionnaire (Heinz et al.,
2009) and UPPS-P (Lynam et al., 2006) are reported in Tables 4 and 5, respectively.
45
Table 3
Weekly Caffeine Consumption Estimates by Gender in Study 2
Portion of the Week Range M SD
Malesa
Total Weekly Consumption 0 - 15,231 3,111 3,181
Weekly Morning Consumption 0 – 11,521 1,856 2,091
Weekly Evening Consumption 0 – 7,989 1,254 1,462
Femalesb
Total Weekly Consumption 7 – 14,670 2,990 2,702
Weekly Morning Consumption 0 – 8,731 1,910 1,794
Weekly Evening Consumption 0 – 11,112 1,080 1,367
Note. Minimum, Maximum and Mean values are round to the nearest milligram. a n = 161. b n = 151.
46
Table 4
Reliability Coefficients for the Caffeine Expectancy Questionnaire in Study 2
Subscale Items M SD α
Acute Negative Effects 8 15.50 4.39 .86
Positive Effects 11 28.04 5.70 .85
Withdrawal Symptoms 11 19.43 6.44 .90
Mood Effects 7 14.04 3.71 .80
Note. 1 = Strongly Disagree 2 = Disagree 3 = Agree 4 = Strongly Agree
47
Table 5
Reliability Coefficients for the UPPS-P
Subscale Items M SD α
Premeditation 11 30.80 5.34 .89
Negative Urgency 12 27.74 6.14 .87
Positive Urgency 14 28.50 7.52 .93
Sensation Seeking 12 35.72 6.75 .88
Perseverance 10 29.82 4.47 .83
Note. 1 = Extremely Uncharacteristic of me 2 = Uncharacteristic of me 3 = Characteristic of me
4 = Extremely Characteristic of me
48
Pearson product-moment correlation coefficients were calculated between potential
predictor variables and the outcome variable and are presented in Table 6. Significant Pearson’s
correlations revealed a potential substance use predictor beyond the subscales of the UPPS-P
(Lynam et al., 2006) and the Caffeine Expectancy Questionnaire (Heinz et al., 2009). For males,
frequency of nicotine consumption (r = .16, p = .05) in the past three months was significantly
positively related to weekly caffeine consumption. No other demographic variables or substance
use variables were significantly correlated with weekly caffeine consumption for either males or
females. Frequency of nicotine consumption was included in further analyses. All expectancy
and impulsivity factors were included in further analyses due to the relative interest in the current
study despite the absence of significant relationships for some of these variables.
49
Table 6
Pearson Correlations with Weekly Caffeine Consumption by Gender
Predictor Males
(n=161)
Females
(n=151)
Age .06 0.10
Drinking Status -.10 .09
Nicotine Use .16* .07
Mood Effects .28*** .22**
Withdrawal Symptoms .22** .23**
Positive Effects .22** .13
Acute Negative Effects -.08 -.09
Premeditation -.17* -.01
Negative Urgency .09 -.03
Positive Urgency .07 -.00
Sensation Seeking .17~ .02
Perseverance -.06 .07
Note. Statistically significant and marginally significant results (α=0.10) are presented in bold text. ~p≤ .1; *p≤ .05; **p≤ .01; ***p≤ .001
50
In order to perform the hierarchical multiple regression analyses, the current research
used R version 2.13.1 to determine if a transformation was needed on the outcome variable
weekly caffeine consumption. A first-order multiple regression model was constructed by
entering 11 predictor variables simultaneously into the program. The 11 predictor variables
included gender, frequency of nicotine consumption in the past three months, the five subscales
of the UPPS-P (Lynam et al., 2006) and the four subscales of the Caffeine Expectancy
Questionnaire (Heinz et al., 2009). Weekly caffeine consumption was used as the outcome
variable. The construction of a first-order multiple regression model was necessary to determine
if a transformation on the response variable was needed. Due to the presence of the value zero in
the response variable, a constant of 1 was added to the response in order to run a Box-Cox
transformation. The Box-Cox transformation is an automatic procedure that determines if a
power transformation on the response is appropriate. The shift in the response was used in all
further analyses. The resulting plot from the Box-Cox suggested that a square root
transformation was needed. A square root transformation on the outcome variable weekly
caffeine consumption was used in all further analyses. No other informal or formal diagnostic
procedures were run using the first-order multiple-regression model that was constructed for the
purpose of examining the possibility of a transformation on the response variable.
The prediction of caffeine consumption by expectancies and impulsivity was examined
by performing a hierarchical multiple-regression analysis. The hierarchical multiple regression
analysis consisted of the 11predictor variables and the transformed outcome variable. The
hierarchical multiple-regression analysis was performed using the program PASW version 18.0.
For the analysis, the predictor variables were entered into the program in three blocks. At each
step, variables added to the model were entered simultaneously. The first step controlled for the
51
variables gender and nicotine frequency. The second step added the expectancy subscales from
the Caffeine Expectancy Questionnaire (Heinz et al., 2009). The final step added the impulsivity
subscales from the UPPS-P (Lynam et al., 2006).
Table 7 provides the results of the hierarchical regression analysis. The model produced
in the first step was significant and accounted for a significant amount of variance in weekly
caffeine consumption, R2 = .03, F (2, 282) = 3.67, p = .027. Specifically, those that consumed
nicotine more frequently were also higher caffeine consumers (β = .16, p= .008). The second
step in which caffeine expectancies were entered into the model accounted for a significant
amount of variance in weekly caffeine consumption above and beyond those variables entered
into the first step, ∆R2 = .15, F (4, 278)= 12.35, p < .001. Mood Effects (β = .23, p = .002) and
Withdrawal Symptoms (β = .16, p = .026) were positively related to weekly caffeine
consumption. Acute Negative Effects (β = -.18, p= .004) were negatively associated with
weekly caffeine consumption. Even with the inclusion of caffeine expectancies, frequency of
nicotine use continued to be significantly associated with weekly caffeine consumption (β = .15,
p = .01). The third step in which impulsivity variables were entered into the model did not
account for a significant amount of variance in weekly caffeine consumption above and beyond
those variables entered in the second step, ∆R2 = .01, F (5, 273) = 0.65, p = .666. Not one
impulsivity variable was significantly associated with weekly caffeine consumption. However,
frequency of nicotine consumption, Mood Effects, Withdrawal Symptoms and Acute Negative
Effects continued to be associated with greater weekly caffeine consumption.
52
Table 7
Hierarchical Regression Models with Expectancy and Impulsivity Factors as Predictors of Caffeine
Consumption Step in regression model ∆R2 F∆R2 df Step 1 Step 2 Step 3
β p-Value for β β p-Value for β β p-Value for β
Step 1 .03 3.67* 282
Gender .06 .314 -.02 .694 -.04 .565
Nicotine Use .16** .008 .15** .010 .14* .019
Step 2 .15 12.35*** 278
Mood Effects .23** .002 .25*** .001
Withdrawal Symptoms .16* .026 .17* .020
Positive Effects .05 .523 .02 .842
Acute Negative Effects -.18** .004 -.15* .018
Step 3
Premeditation .01 0.65 273 -.09 .179
Negative Urgency .04 .633
Positive Urgency -.08 .327
Sensation Seeking .01 .946
Perseverance .06 .317
Note. Regression analyses were performed using the square root transformed outcome variable, weekly caffeine consumption. Statistically significant results (α=0.05) are presented in bold text. *p≤ .05; **p≤ .01; ***p≤ .001
53
Given that for males significant and marginally significant correlation coefficients were
found between two impulsivity dimensions (i.e., Premeditation and Sensation Seeking) and
weekly caffeine consumption, the current research conducted data driven exploratory
hierarchical multiple regression analyses to determine the presence of possible interactions.
Gender was effect coded (male = -1, female =1). Scores on the continuous variables
Premeditation and Sensation Seeking were centered. Interaction terms were computed as
products of the centered continuous variables and the dichotomous variable, gender, which was
effect coded. A hierarchical multiple regression analysis was conducted to explore each
interaction term. Each regression analysis consisted of four blocks. The first step included
variables that were previously found to be significant in the full model and included frequency of
nicotine consumption, Withdrawal Symptoms, Acute Negative Effects, and Mood Effects. The
second step in the model entered gender, effect coded. The third step included the centered
impulsivity variable. The fourth step in the model entered the interaction term. A marginally
significant interaction emerged concerning gender and Premeditation, β = .11, t = 1.92, p = .055
and uniquely accounted for an additional 1.1% of the variance in weekly caffeine consumption.
To better understand this interaction, separate regression analyses were performed for males and
females. The results of these analyses indicate that only male scores on the Premeditation
subscale were uniquely related to weekly caffeine consumption, β = -.14, t = -1.94, p = .055,
such that as scores on the subscale Premeditation decrease for males, weekly caffeine
consumption increases. For females, the inverse was seen, that is, as premeditation scores
increased, weekly caffeine consumption increased. However, this positive association for
females was not statistically significant, β = .06, t = .68, p = .498. The gender by Sensation
Seeking interaction was not significant, β = -.06, t = -1.18, p = .239.
54
23
24
25
26
27
28
Week
ly C
aff
ein
e C
on
sum
pti
on
Males
Females
1 -1 Standardized Premeditation
Scores
Figure 1. Gender and Premeditation Interaction
Figure 1. Regression analyses were performed using the square root transformed outcome
variable, weekly caffeine consumption.
55
Participants’ typical weekly caffeine consumption was assessed using the CCQ 2010 in
Part I. One week later, Part II, participants were asked to complete another version of the CCQ
2010 that instructed them to reflect on the previous week, which produced an estimate of
participants’ last week caffeine consumption. To determine the degree to which participants’
self-reported typical weekly caffeine consumption was related to their self-reported last week
caffeine consumption, a Pearson product-moment correlation coefficient was calculated. The
correlation coefficient between the variables typical weekly caffeine consumption and last week
caffeine consumption was relatively high (r = .65, p < .001).
Discussion
The current study predicted that caffeine expectancies measured by the Caffeine
Consumption Questionnaire (Heinz et al., 2009) and impulsivity measured by the UPPS-P
(Lynam et al., 2006) would predict weekly caffeine consumption. Specifically, it was
hypothesized that the Withdrawal Symptoms subscale of Caffeine Expectancy Questionnaire
would relate to higher caffeine consumption. This hypothesis was supported by the hierarchical
regression analysis in the expected direction. Furthermore, based on previous research, it was
expected that the Sensation Seeking subscale would be the best predictor of weekly caffeine
consumption of the impulsivity factors, and this association was expected to be positive. This
hypothesis was not supported by the hierarchical regression analysis. Not only was sensation
seeking not a significant predictor, the impulsivity subscales did not explain significantly more
variance than the expectancy subscales and frequency of nicotine consumption. However, a data
driven exploratory hierarchical multiple regression analysis, did reveal the presence of a
marginally significant gender and Premeditation interaction.
56
SUMMARY AND CONCLUDING DISCUSSION
The current research examined multiple variables that were hypothesized to be associated
with caffeine consumption in a college sample. Currently, caffeine intake is not thoroughly
understood, especially as it relates to personality traits. Although, Landrum (1992) developed
the original CCQ with the intent that it could be used in a variety of settings which would
provide consistency across research, its modification was deemed necessary to continue the
process of an accurate portrayal of self-reported caffeine consumption. The current research
modified the CCQ (Landrum, 1992), termed the CCQ 2010, to provide a self-report instrument
to assess caffeine consumption using modern day vehicles. The implications of the use of the
CCQ 2010 are many, including the continued use of a measure to provide consistency across
research settings, thus, allowing for better comparisons in relation to caffeine consumption
especially in college samples. Furthermore, the CCQ 2010 may have practical and clinical
applications. The CCQ 2010 could be used in clinical settings to provide feedback for
individuals who may benefit from restricting their caffeine intake. For example, a literature
review conducted by Vilarim, Rocha Araujo and Nardi (2011) suggested that consumption of
caffeine by populations endorsing anxiety symptoms may exacerbate these symptoms.
Therefore, individuals diagnosed with anxiety disorders may benefit from a heightened
awareness of their weekly caffeine intake and vehicles that contain caffeine. The current study
modified a measure of caffeine consumption that not only represents the present availability of
caffeine vehicles, but also one that could be used in real-world instances where an estimate of
weekly caffeine consumption could be used to provide feedback to clients that would benefit
from a heightened awareness of their consumption of this psychostimulant.
57
In order to examine and understand caffeine consumption, it was necessary to study
caffeine expectancies. The current research used a relatively new instrument for the assessment
of caffeine expectancies, the Caffeine Expectancy Questionnaire (Heinz et al., 2009), thus,
providing a clearer picture of the caffeine consumption and expectancy phenomenon. The
present study extends the literature on caffeine expectancies by demonstrating, through
hierarchical multiple regression analysis, their ability to predict self-reported weekly caffeine
intake in a college sample. The first hypothesis of the current study was that caffeine
expectancies as measured by the Caffeine Expectancy Questionnaire (Heinz et al., 2009) would
predict caffeine consumption. The results from the hierarchical multiple regression analysis
performed in Study 2 support this hypothesis. On the second step of the hierarchical multiple
regression analysis the four caffeine expectancy subscales were entered simultaneously and
accounted for a significant amount of variance above and beyond frequency of nicotine
consumption and gender. Specifically, it was predicted that those scoring higher on the
Withdrawal Symptoms subscale would also be higher caffeine consumers. This hypothesis was
also supported. The Withdrawal Symptoms subscale was uniquely associated with higher
caffeine consumption. Predictions regarding the relative contributions of the other expectancy
factors were not generated and this information adds to the literature on caffeine expectancies.
The Mood Effects subscale was positively related to weekly caffeine consumption, whereas the
Acute Negative Effects subscale was negatively associated to weekly caffeine consumption. The
positive association between the Mood Effects subscale and weekly caffeine consumption
suggests that endorsing greater expectancies for positive mood when consuming caffeine
predicts greater weekly caffeine consumption. On the other hand, the negative association
between the Acute Negative Effects subscale and weekly caffeine consumption suggests that
58
endorsing greater expectancies for unpleasant effects such as jitteriness predicts lower weekly
caffeine consumption. The Positive Effects subscale was not uniquely related to weekly caffeine
consumption. The results regarding the relationship between the expectancy factors and weekly
caffeine consumption provide new information as to how these concepts are related, especially
given that the Caffeine Expectancy Questionnaire (Heinz et al., 2009) is a relatively new
measure. Future research should aim to replicate these findings with college samples in other
geographic locations.
The second hypothesis of the current study was that impulsivity dimensions would
predict caffeine consumption in a college sample. The present study is the first of its kind to use
the UPPS-P (Lynam et al., 2006) to examine the relationship of impulsivity, assuming five
dimensions, to weekly caffeine consumption. Although, caffeine intake, among other variables,
has been studied in relation to personality, none has attempted to use this five factor model of
impulsivity. The third step of the hierarchical multiple regression analysis conducted in Study 2
indicated that the impulsivity factors did not account for a significant amount of variance above
and beyond frequency of nicotine consumption and caffeine expectancies. The second
hypothesis was not supported. Given that the impulsivity dimensions did not account for a
significant amount of additional variance, there was no evidence for the specific prediction that
sensation seeking would be positively associated to caffeine consumption. However, based on
the significant and marginally significant Pearson product moment correlations that were
conducted by gender, data driven exploratory analyses were performed to determine if there was
evidence of gender interactions with two of the impulsivity subscales, namely, Premeditation and
Sensation Seeking. Although the hierarchical multiple regression analysis exploring a possible
gender by Sensation Seeking interaction was not significant, the gender by Premeditation
59
interaction was marginally significant. Specifically for males, lower scores on the Premeditation
subscale were predictive of higher weekly caffeine consumption and this relationship was
marginally significant. The Premeditation subscale measures the tendency for an individual to
delay immediate behavior in service of forethought and planning. Thus, lower scores on this
subscale are in line with what would be characterized as impulsive behavior. Therefore, the
marginally significant interaction suggests that for males who engaged in less forethought and
planning, higher weekly caffeine consumption was more likely. For females, there was a
positive association between Premeditation and weekly caffeine consumption, but it was not
significant. Given that these analyses were data driven, it should be noted that the interaction
findings presented here are capitalizing on chance. Future research should aim to replicate these
findings and examine why male caffeine consumption might be negatively associated with
Premeditation.
Given that over half of the sample self-reported weekly caffeine consumption estimates
below the mean (64.4%) and that a square root transformation was needed to assume normality
of the response, it may be that low to moderate caffeine consumption is not predicted by
impulsivity dimensions. The current study proposed a model of augmented caffeine
consumption by synthesizing available research regarding trends in impulsive populations,
treatment for ADHD and the dopaminergic effects of caffeine intake. The augmented caffeine
consumption model would be more likely to predict a relationship between caffeine consumption
and impulsivity for those at the higher end of the caffeine consumption and impulsivity
distributions, and there is evidence to suggest that these individuals were underrepresented.
Future research may aim to procure the participation of not only higher caffeine consumers but
also populations where an impulsivity relation is known to exist, such as those diagnosed with
60
ADHD. Such research would further clarify the relationship between impulsivity and caffeine
consumption.
There were limitations to the current study. The CCQ 2010 was developed with the
intention to provide as many memory cues as possible in an attempt to obtain accurate self-report
data on caffeine consumption. To this end, the instrument was divided such that participants
were asked to provide a typical weekday estimate and a typical weekend day estimate.
Additionally, consistent with Landrum’s (1992) original iteration, time of day was broken down
into four categories. Furthermore, to aid in accurate completion of the questionnaire, Microsoft
PowerPoint 2007 was used as a visual aid and demonstrated correct and incorrect examples of
completion. Although the current research is confident that the CCQ 2010 administration was
clear, it is unknown whether participants completely understood the instructions. Therefore,
subsequent research with this instrument should include a screening questionnaire after the
presentation of the instructions for the instrument. Such a screening measure could include
written scenarios of caffeine consumers and their consumption during a typical week. A series
of multiple choice questions based on the written scenarios would clearly demonstrate the
participants’ understanding of the instructions. Such a screening measure would ensure the
collected data was in fact measuring what was intended.
Additionally, the generalizability of these results are limited given that the sample
recruited in the present study included participants from one southeastern university in the
United States, and it is difficult to say how these results would generalize to populations in other
geographic areas. Replication of these results to other college populations is needed.
The current study also included longitudinal information regarding the stability of the
CCQ 2010. It is still unknown whether the act of completing the CCQ 2010 in fact changes
61
behavior. Although a subsequent administration of the measure was given, the directions were
not identical to the first administration and do not speak to the measure’s test-retest reliability.
Although, the Pearson product moment correlation does suggest stability, given that the
correlation was relatively high despite the difference in the administration of the questionnaires.
The current research modified a caffeine consumption instrument with the aim of
producing a measure that would reflect modern day availability. Based on the available
literature, the present study generated hypotheses concerning potential predictors of caffeine
consumption. Specifically, it was suggested that caffeine expectancies and impulsivity
dimensions would predict weekly caffeine consumption in a college sample. These hypotheses
were partially supported. Caffeine expectancies were found to account for a significant amount
of variance in weekly caffeine consumption. However, impulsivity dimensions were not
predictive of weekly caffeine consumption. A data driven exploratory analysis revealed a
marginally significant interaction between gender and Premeditation. Specifically, for males, as
Premeditation scores decreased, weekly caffeine consumption increased. Although the results
from the current research suggest that caffeine expectancies are good predictors of weekly
caffeine consumption and that impulsivity dimensions do not account for a significant amount of
variance above and beyond expectancies, it is possible that higher consumers of caffeine were
underrepresented in the current sample. Future research should aim to procure the participation
of higher caffeine consumers and aim to replicate the gender and Premeditation interaction, to
further elucidate the association between impulsivity and caffeine consumption.
62
REFERENCES
Advokat, C. (2009). What exactly are the benefits of stimulants for ADHD? Journal of
Attention Disorders, 12, 495-498. doi:10.1177/1087054708329781
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental
disorders (4th
ed., Text Revision). American Psychiatric PressWashington, DC.
Barone, J. J., & Roberts, H. R. (1996). Caffeine consumption. Food and Chemical Toxicology,
34, 119-129.
Brown, S. A. (1993). Drug effect expectancies and addictive behavior change. Experimental
and Clinical Psychopharmacology, 1, 55-67. doi:10.1037/1064-1297.1.1-4.55
Brunyé, T. T., Mahoney, C. R., Lieberman, H. R., & Taylor, H. A. (2010). Caffeine modulates
attention network function. Brain and Cognition, 72, 181-188. doi:10.1016 /j.bandc.200
9.07.013
Buss, A. H. & Plomin, R. (1975). A temperament theory of personality development. New
York: John Wiley & Sons.
Cahalan, D., Cisin I. H., & Crossley, H. M. (1969). American drinking practices: a national
study of drinking behavior and attitudes. Publications Division, Rutgers Center of
Alcohol Studies; distributed by College & University Press, New Haven, Conn., New
Brunswick, N.J.
Cloninger, C. R., Przybeck, T. R. & Svrakic, D. M. (1991). The Tridimensional Personality
Questionnaire: US normative data. Psychological Reports, 69, 1047-1057.
Cloninger, C. R., Przybeck, T. R., Svrakic, D. M., & Wetzel, R. M. (1994). The temperament
and character inventory (TCI): A guide to its development and use. St. Louis (MO):
Center for Psychobiology of Personality, Washington University.
63
Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C. (2007).
Integration of impulsivity and positive mood to predict risky behavior: Development and
validation of a measure of positive urgency. Psychological Assessment, 19, 107-118.
doi:10.1037/1040-3590.19.1.107
Dickman, S. J. (1990). Functional and dysfunctional impulsivity: Personality and cognitive
correlates. Journal of Personality and Social Psychology, 58, 95-102.
Ernst, M., Pine, D. S., & Hardin, M. (2006). Triadic model of the neurobiology of motivated
behavior in adolescence. Psychological Medicine, 36, 299-312. doi:10.1017/S0033291
705005891
Evenden, J. L. (1999). Varieties of impulsivity. Psychopharmacology, 146, 348-361.
Eysenck, H. J. & Eysenck, S. B. G. (1975). Manual of the Eysenck Personality Questionnaire.
London: Hodder & Stoughton.
Eysenck, S. B. G. (1993). The I7: Development of a measure of impulsivity and its relationship
to the superfactors of personality. In: W. G. McCown, J. L. Johnson & M. B. Shure
(Eds.), The impulsive client: Theory, research and treatment (pp. 141-149). American
Psychological Association: Washington, D.C.
Eysenck, S. B., Pearson, P. R., Easting, G., & Allsopp, J. F. (1985). Age norms for
impulsiveness, venturesomeness, and empathy in adults. Personality and Individual
Differences, 6, 613-619.
Ferré, S. (2008). An update on the mechanisms of the psychostimulant effects of caffeine.
Journal of Neurochemistry, 105, 1067-1079. doi:10.1111/j.1471-4159.2007.05196.x
64
Fillmore, M. T., Mulvihill, L. E., & Vogel-Sprott, M. (1994). The expected drug and its
expected effect interact to determine placebo responses to alcohol and caffeine.
Psychopharmacology, 115, 383-388.
Fillmore, M., & Vogel-Sprott, M. (1992). Expected effect of caffeine on motor performance
predicts the type of response to placebo. Psychopharmacology, 106, 209-214.
Fillmore, M. T., & Vogel-Sprott, M. (1994). Psychomotor performance under alcohol and
under caffeine: Expectancy and pharmacological effects. Experimental and Clinical
Psychopharmacology, 2, 319-327.
Goldstein, R. Z., Alia-Klein, N. & Volkow, N. D. (2009). Drug addiction: Neuroimaging.
In L. R. Squire (Ed.), The Encyclopedia of Neuroscience (pp. 699-711).
Gurpegui, M., Jurado, D., Luna, J. D., Fernández-Molina, C., Moreno-Abril, O., & Gálvez, R.
(2007). Personality traits associated with caffeine intake and smoking. Progress in
Neuro-Psychopharmacology & Biological Psychiatry, 31, 997-1005.
Harrell, P.l T., & Juliano, L. M. (2009). Caffeine expectancies influence the subjective and
behavioral effects of caffeine. Psychopharmacology, 207, 335-342. doi:10.1007/s0
0213-009-1658-5
Heatherly, S. V., Hayward, R. C., Seers, H. E., & Rogers, P. J. (2005). Cognitive and
psychomotor performance, mood, and pressor effects of caffeine after 4, 6, and 8 h
caffeine abstinence. Psychopharmacology, 178, 461-470.
Heatherton, T. F. & Wagner, D. D. (2011). Cognitive neuroscience of self-regulation failure.
Trends in Cognitive Sciences, 15, 132-139. doi:10.1016/j.tics.2010.12.005
65
Heinz, A. J., Kassel, J. D., & Smith, E. V. (2009). Caffeine expectancy: Instrument development
in the rasch measurement framework. Psychology of Addictive Behaviors, 23, 500
-511. doi:10.1037/a0016654
Horne, J. A., & Ostberg, O. (1975). Time of day effects on extroversion and salivation.
Biological Psychology, 3, 301-307.
Jackson, D. N. (1984). Personality research form manual. Goshen, NY: Research
Psychologists Press.
Jones, H. A., & Lejuez, C. W. (2005). Personality correlates of caffeine dependence: The role of
sensation seeking, impulsivity, and risk taking. Experimental and Clinical
Pscyhopharmacology, 13, 259-266. doi:10.1037/1064-1297.13.3.259
Khantzian, E. J. (1985). The self-medication hypothesis of addictive disorders: Focus on heroin
and cocaine dependence. American Journal of Psychiatry, 142, 1259-1264.
Khantzian, E. J. (2003). Understanding addictive vulnerability: An evolving psychodynamic
perspective. Neuropsychoanalysis, 5, 5-21.
Landrum, R. E. (1992). College students’ use of caffeine and its relationship to personality.
College Student Journal, 26, 151-155.
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: Applied, 8, 75-84.
Lodge, D.J., Buffalari, D.M. & Grace, A.A. (2009) Dopamine: CNS pathways and
neurophysiology. In L. R. Squire (Ed.) Encyclopedia of Neuroscience (pp. 549-555).
66
Lynam, D.R., & Miller, J. D. (2004). Personality pathways to impulsive behavior and their
relations to deviance: Results from three samples. Journal of Quantitative Criminology,
20, 319-341.
Lynam, D. R., Smith, G. T., Whiteside, S. P., & Cyders, M. A. (2006). The UPPS-P: Assessing
five personality pathways to impulsive behavior (Technical Report). West Lafayette:
Purdue University.
Magid, V. & Colder, C. R. (2007). The UPPS Impulsive Behavior Scale: Factor structure
associations with college drinking. Personality and Individual Differences, 43, 1927
-1937.
McCrae, R. R. & Costa, P. T. Jr (1990). Personality in adulthood. New York: Guilford.
Oei, A., & Hartley, L. R. (2005). The effects of caffeine and expectancy on attention and
memory. Human Psychopharmacology, 20, 193-202. doi:10.1002/hup.681
Patton, J. H., Stanford, M.S. & Barratt, E. S. (1995). Factor structure of the Barratt
Impulsiveness Scale. Journal of Clinical Psychology, 51, 768-774.
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen:
Danish Institute for Educational Research. (Expanded edition, 1980. Chicago:
University of Chicago Press)
Reissig, C. J., Strain, E. C., & Griffiths, R. R. (2009). Caffeinated energy drinks-A growing
problem. Drug and Alcohol Dependence, 99, 1-10. doi:10.1016/j. drugalcdep.2008
.08.001
Schwartz, R. M, Burkhart, B. R. & Green, S. B. (1978). Turning on or turning off: Sensation
seeking or tension reduction as motivational determinants of alcohol use. Journal of
Consulting and Clinical Psychology, 46, 1144-1145.
67
Shohet, K. L., & Landrum, R. E. (2001). Caffeine consumption questionnaire: A standardized
measure for caffeine consumption in undergraduate students. Psychological Reports, 89,
521-526.
Smith, G. T., Fishcher, S., Cyders, M. A., Annus, A. M., Spillane, N. S., & McCarthy, D. M.
(2007). On the validity and utility of discriminating among impulsivity-like traits.
Assessment, 14, 155-170. doi:10.1177/1073191106295527
Spillane, N. S., Smith, G. T., & Kahler, C. W. (2010). Impulsivity-like traits and smoking
behavior in college students. Addictive Behaviors, 35, 700-705. doi:10.1016/ j.add
beh.2010.03.008
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking.
Developmental Review, 28, 78-106. doi:10.1016/j.dr.2007.08.002
Steinberg, L. (2010). A dual systems model of adolescent risk-taking. Developmental
Psychobiology, 52, 216-224. doi:10.1002/dev.20445
Tang, A., Wanchoo, S. J., Swann, A. C., & Dafny, N. (2009). Psychostimulant treatment for
ADHD is modulated by prefrontal cortex manipulation. Brain Research Bulletin, 80,
353-358. doi:10.1016/j.brainresbull.2009.08.022
Tellegen, A. (1982). Multidimensional Personality Questionnaire manual. Minneapolis, MD:
University of Minnesota Press.
Vilarim, M. M., Rocha Araujo, D. M. & Nardi, A. E. (2011). Caffeine challenge test and panic
disorder: A systematic literature review. Expert Reviews Neurotherapeutics, 11, 1185
-1195. doi:10.1586/ERN.11.83
68
Whiteside, S. P. & Lynam, D. R. (2001). The five factor model and impulsivity: Using a
structural model of personality to understand impulsivity. Personality and Individual
Differences, 30, 669-689.
Wieder, H., & Kaplan, E. H. (1969). Drug use in adolescents: Psychodynamic meaning and
pharmacogenic effect. Psychoanalytic study of the child, 24, 399-431.
Williamson, A. (2007). Using self-report measures in neurobehavioural toxicology: Can they be
trusted? NeuroToxicology, 28, 227-234. doi:10.1016/j.neuro.2006.03.009
Yeomans, M., Ripley, T., Davies, L., Rusted, J., & Rogers, P. (2002). Effects of caffeine on
Performance and mood depend on the level of caffeine abstinence.
Psychopharmacology, 164, 241-249.
Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. New
York: Cambridge University Press.
Zuckerman, M., Eysenck, S. B., & Eysenck, H. J. (1978). Sensation seeking in England and
America: Cross-cultural, age, and sex comparisons. Journal of Consulting and Clinical
Psychology, 46, 139-149. doi:10.1037/0022-006X.46.1.139
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APPENDIX
Appendix A. Demographics Questionnaire for Study 1
A CLOSE LOOK AT CAFFEINE We are interested in studying caffeine. In order to do so, we need you to honestly and thoroughly answer the questions presented in the present study. Your participation is entirely voluntary. At any time you may stop your involvement or decline to answer any question without being treated differently by the researcher. Any document received from the researcher is ANONYMOUS. Please do not write your name on any of the forms presented to you today. If you have any questions about this study or want to learn more about the results, please contact Jennifer Heaton JAH7711@UNCW.EDU or Dr. Nora Noel NNOEL@UNCW.EDU in the psychology department. In addition, if you have questions about your rights as a participant, you may contact the Chair of the UNCW Institutional Review Board, Dr. Candace Gauthier (Gauthierc@uncw.edu). Thank you for your participation. First, please tell us some things about yourself Today’s date ____/____/____ Age__________ Height________ Weight_________ Sex___________ -Current Marital/Dating Status (please circle one) Married/cohabitating Divorced/Separated Steady Dating Someone Single Other -Current Educational Status (please circle one) Less than High School High School Graduate College Freshman College Sophomore College Junior College Senior Other_________________________________ -Are you a Full Time or Part Time Student? -Your Ethnic Background: __________________________________________________
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SUBSTANCE USE How often have you used any of these psychoactive substances in the LAST THREE MONTHS? Code frequency of use according to the following: 0= Never 1= 1 or 2 times in the last three months 2= once per month 3= once every two weeks 4= once per week 5= 2-4 times per week 6= almost everyday Substance Prescribed Medications ______Alcohol ______Birth Control ______Caffeine ______Amphetamines ______Nicotine ______Barbiturates ______Marijuana ______Benzodiazapines ______Hashish ______Other Tranquilizers ______Crack ______Opiates (e.g. Methadone, Darvon) ______Cocaine ______Anti-depressants ______Amphetamines (not prescribed) ______Anti-psychotics ______Barbiturates (not prescribed) ______Anti-manic (e.g. Lithium) ______Benzodiazapines (not prescribed) ______Other psychoactive medication ______Other Tranquilizers (not prescribed) ______Heroin ______Other Opiates (not prescribed) ______Hallucinogens ______Inhalants ______Any drugs by injection ever
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Appendix B. Pilot Caffeine Consumption Questionnaire Typical Weekday for Study 1
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Appendix C. Pilot Caffeine Consumption Questionnaire Typical Weekend Day for Study 1
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Appendix D. Informed Consent for Study 2
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Appendix E. Code Name Form: Experimenter’s Copy for Study 2
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Appendix F. Code Name Form- Participant’s Copy for Study 2
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Appendix G. Code Name and Telephone Number Form for Study 2
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Appendix H. Demographics Questionnaire for Study 2
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Appendix I. General Caffeine Questions for Study 2
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Appendix J. Previous Week General Caffeine Questions for Study 2
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Appendix K. Typical Week Caffeine Consumption Questionnaire (2010) for Study 2
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Appendix L. Previous Week Caffeine Consumption Questionnaire (2010) for Study 2
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Appendix M. Modified Quantity Frequency Index for Study 2
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