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Academic procrastination in college students: The role of self-reported executive function

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Procrastination, or the intentional delay of due tasks, is a widespread phenomenon in college settings. Because procrastination can negatively impact learning, achievement, academic self-efficacy, and quality of life, research has sought to understand the factors that produce and maintain this troublesome behavior. Procrastination is increasingly viewed as involving failures in self-regulation and volition, processes commonly regarded as executive functions. The present study was the first to investigate subcomponents of self-reported executive functioning associated with academic procrastination in a demographically diverse sample of college students aged 30 years and below (n = 212). We included each of nine aspects of executive functioning in multiple regression models that also included various demographic and medical/psychiatric characteristics, estimated IQ, depression, anxiety, neuroticism, and conscientiousness. The executive function domains of initiation, plan/organize, inhibit, self-monitor, working memory, task monitor, and organization of materials were significant predictors of academic procrastination in addition to increased age and lower conscientiousness. Results enhance understanding of the neuropsychological correlates of procrastination and may lead to practical suggestions or interventions to reduce its harmful effects on students' academic performance and well-being.
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JOURNAL OF CLINICAL AND EXPERIMENTAL NEUROPSYCHOLOGY
2011, 33 (3), 344–357
Academic procrastination in college students: The role
of self-reported executive function
Laura A. Rabin1, Joshua Fogel2, and Katherine E. Nutter-Upham1
1Department of Psychology, Brooklyn College and the Graduate Center of the City University of
New York, Brooklyn, NY, USA
2Department of Finance and Business Management, Brooklyn College of the City University of
New York, Brooklyn, NY, USA
Procrastination, or the intentional delay of due tasks, is a widespread phenomenon in college settings. Because
procrastination can negatively impact learning, achievement, academic self-efficacy, and quality of life, research
has sought to understand the factors that produce and maintain this troublesome behavior. Procrastination is
increasingly viewed as involving failures in self-regulation and volition, processes commonly regarded as execu-
tive functions. The present study was the first to investigate subcomponents of self-reported executive functioning
associated with academic procrastination in a demographically diverse sample of college students aged 30 years
and below (n=212). We included each of nine aspects of executive functioning in multiple regression models
that also included various demographic and medical/psychiatric characteristics, estimated IQ, depression, anxiety,
neuroticism, and conscientiousness. The executive function domains of initiation, plan/organize, inhibit, self–
monitor, working memory, task monitor, and organization of materials were significant predictors of academic
procrastination in addition to increased age and lower conscientiousness. Results enhance understanding of the
neuropsychological correlates of procrastination and may lead to practical suggestions or interventions to reduce
its harmful effects on students’ academic performance and well-being.
Keywords: Academic procrastination; Executive functioning; Conscientiousness.
INTRODUCTION
Academic procrastination—the intentional delay in the
beginning or completion of important and timely aca-
demic activities (Schouwenburg, 2004; Ziesat, Rosenthal,
& White, 1978)—is a widespread phenomenon in col-
lege settings. Approximately 30% to 60% of undergrad-
uate students report regular postponement of educa-
tional tasks including studying for exams, writing term
papers, and reading weekly assignments, to the point
at which optimal performance becomes highly unlikely
(Ellis & Knaus, 1977; Janssen & Carton, 1999; Kachgal,
Hansen, & Nutter, 2001; Onwuegbuzie, 2004; Pychyl,
Lee, Thibodeau, & Blunt, 2000a; Pychyl, Morin, &
Salmon, 2000b; Solomon & Rothblum, 1984). While
occasional delays are acceptable and may even be advan-
tageous, what distinguishes problematic or habitual pro-
crastination from merely deciding to perform an activity
Address correspondence to Laura A. Rabin, Department of Psychology, Brooklyn College, 2900 Bedford Avenue,Brooklyn, NY,
USA (E-mail: lrabin@brooklyn.cuny.edu).
at some later time is the accompanying internal subjective
discomfort (Lay & Schouwenburg, 1993). This discom-
fort may manifest as anxiety, irritation, regret, despair,
or self-blame (Burka & Yuen, 1983; Pychyl et al., 2000a;
Rothblum, Solomon, & Murakami, 1986). There are also
external consequences to chronic academic procrastina-
tion such as compromised performance and progress,
decreased learning, lost opportunities, increased health
risks, and strained relationships (Beswick, Rothblum, &
Mann, 1988; Burka & Yuen; Burns, Dittman, Nguyen, &
Mitchelson, 2000; Moon & Illingworth, 2005a; Rothblum
et al., 1986; Tice & Baumeister, 1997).
Due to these significant negative aspects, researchers
have studied procrastination and have proposed vari-
ous cognitive, emotional, and personality variables as
possible predictors. Frequently cited cognitive correlates
include a tendency toward self-handicapping, low self-
esteem, low academic self-efficacy, fear of failure, and
©2010 Psychology Press, an imprint of the Taylor & Francis Group, an Informa business
http://www.psypress.com/jcen DOI: 10.1080/13803395.2010.518597
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EXECUTIVE DYSFUNCTION AND ACADEMIC PROCRASTINATION 345
distorted perceptions of available and required
time to complete tasks (Ferrari, Parker, & Ware,
1992; Ferrari & Tice, 2000; Judge & Bono, 2001;
Kachgal et al., 2001; Lay, 1988, 2004; Pychyl, Coplan,
& Reid, 2002). With regard to emotional functioning,
several studies have found that anxiety, depression,
and worry are associated with procrastination (Antony,
Purdon, Huta, & Swinson, 1998; Ferrari, Johnson, &
McCown, 1995; Stoeber & Joormann, 2001; van Eerde,
2003), but the empirical evidence concerning the relation-
ship between mood and procrastination is not definitive
(Steel, 2007). In terms of personality features, research
has consistently shown that lower conscientiousness and,
to a lesser extent, higher neuroticism are related to trait
procrastination (Johnson & Bloom, 1995; Lee, Kelly, &
Edwards, 2006; Milgram & Tenne, 2000; Schouwenburg
& Lay, 1995; van Eerde, 2003).
Various demographic and medical/psychiatric vari-
ables have also been examined in relation to procrasti-
nation. Research is inconclusive with regard to whether
academic procrastination is related to students’ gen-
der (Steel, 2007; van Eerde, 2003). Although several
studies have reported higher levels in males (Milgram,
Marshevsky, & Sadeh, 1994; Ozer, Demir, & Ferrari,
2009; Senécal, Koestner, & Vallerand, 1995), many others
have reported no such gender differences (Ferrari, 2001;
Ferrari et al., 1992; Haycock, McCarthy, & Skay, 1998;
Hess, Sherman, & Goodman, 2000; Kachgal et al., 2001).
Research is similarly inconclusive regarding age, with
some studies reporting negative correlations (Beswick
et al., 1988; Prohaska, Morrill, Atiles, & Perez, 2000; van
Eerde, 2003) and others reporting no meaningful corre-
lations (Haycock et al., 1998; Howell, Watson, Powell, &
Buro, 2006) between age and procrastination. Ethnicity
(Clark & Hill, 1994; Kachgal et al., 2001; Prohaska
et al., 2000) and level of intelligence (Ferrari, 1991a; van
Eerde, 2003) do not seem to be related to procrastina-
tion, though there is little extant research on the relation
between these variables. Students with drug and alcohol
problems (Jamrozinski, Kuda, & Mangholz, 2009), learn-
ing disabilities (Klassen, Krawchuk, Lynch, & Rajani,
2008a), and attentional problems (Safren, 2006; Steel,
2007; Weiss & Murray, 2003) have also been reported to
exhibit heightened levels of procrastination.
Procrastination is increasingly recognized as involving
a failure in self-regulation such that procrastinators, rel-
ative to nonprocrastinators, may have a reduced ability
to resist social temptations, pleasurable activities, and
immediate rewards when the benefits of academic prepa-
ration are distant (Ariely & Wertenbroch, 2002; Chu
& Choi, 2005; Dewitte & Schouwenburg, 2002; Ferrari,
2001; Schouwenburg & Groenewoud, 2001; Tan et al.,
2008; Van Eerde, 2000; Wolters, 2003). These individuals
also fail to make efficient use of internal and exter-
nal cues to determine when to initiate, maintain, and
terminate goal-directed actions (Senécal et al., 1995).
Associated characteristics include reduced agency, disor-
ganization, poor impulse and emotional control, poor
planning and goal setting, reduced use of metacogni-
tive skills to monitor and control learning behavior,
distractibility, poor task persistence, time and task
management deficiencies, and an intention–action gap
(Dewitte & Lens, 2000; Dewitte & Schouwenburg, 2002;
Ferrari & Emmons, 1995; Shanahan & Pychyl, 2007;
Steel, 2007; Steel, Brothen, & Wambach, 2001; Tan et al.,
2008; Wolters, 2003).
Research implicates frontal brain systems in self-
regulatory and related processes, and this is generally
referred to as executive functioning (Roth, Randolph,
Koven, & Isquith, 2006; Stuss & Benson, 1986). Executive
functions comprise various neurocognitive processes that
enable novel problem solving, modification of behavior
in response to new information, planning and generat-
ing strategies for complex actions, and the self-regulation
of cognition, behavior, and emotion (Roth et al., 2006;
Williams, Suchy, & Rau, 2009). Given the role of exec-
utive functioning in the initiation and completion of
complex behaviors, it is surprising that little research
examines the relationship between executive function-
ing and academic procrastination. Extant research is
limited and/or indirect. For example, Schouwenburg
(2004) found that procrastination was inversely corre-
lated with adoption of a systematic and disciplined
approach to one’s work and with planning and managing
of one’s time, suggestive of poor organization. Wolters
(2003) showed that procrastination correlated with stu-
dents’ self-efficacy and self-regulated learning strategies.
Howell and Watson (2007) found that lower cognitive and
metacognitive strategy usage and disorganization pre-
dicted procrastination in a sample of college students.
Strub (1989) described the case of a 60-year-old man who
developed chronic procrastination following a cerebral
hemorrhage that resulted in frontal lobe syndrome. Thus,
some evidence of associations between frontal system
network dysfunction and procrastination has emerged,
though it remains unclear which specific aspects of exec-
utive functioning are most implicated in delay behaviors.
To our knowledge, the only direct investigation of exec-
utive dysfunction as a source of procrastination is an
unpublished doctoral thesis, which did not find a sig-
nificant relationship between neuropsychological tests of
executive functioning and severe academic procrastina-
tion (Stone, 1999). To address this gap in the literature
and to capture more everyday aspects of executive func-
tioning, the current study investigated the extent to which
procrastination was predicted by self-reported executive
functioning in a sample of college undergraduates. We
carried out separate analyses for each of nine clinical sub-
scales of a self-report measure of executive functioning,
the Behavior Rating Inventory of Executive Function–
Adult Version (BRIEF-A; Roth, Isquith, & Gioia, 2005).
In our first model, we included each of the nine BRIEF-
A subscales plus demographic and medical/psychiatric
variables. In a second model, we included the first set
of variables plus estimated IQ and relevant personal-
ity and mood variables. We hypothesized that BRIEF-A
subscales tapping inhibitory control/impulsivity, self-
monitoring, planning and organization skills, and
task initiation would be significant predictors of aca-
demic procrastination. Conscientiousness, neuroticism,
and mood symptoms were also hypothesized to be
significant predictors of academic procrastination. We
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346 RABIN, FOGEL, NUTTER-UPHAM
did not generate specific hypotheses concerning the rela-
tion between procrastination and gender, age, ethnicity,
alcohol or drug use, or various medical/psychiatric con-
ditions, given the equivocal nature of findings or paucity
of research on these variables.
METHOD
Participants and procedure
Data were collected from February 2007 through
February 2009. Participants (n=212) were drawn from
various undergraduate psychology courses at a four-year
public college that is part of a large, urban university
system. Students were offered partial class credit or the
chance to win a $50 gift certificate as compensation
for participation. Students were informed that the study
entailed completing a series of paper-and-pencil ques-
tionnaires that would take approximately 30–40 min on
the general topic of academic motivation. Participation
was voluntary and confidential, and informed consent
was obtained according to an institutional review board
(IRB)-approved protocol.
Our sample was obtained from a larger sample of 243
individuals. Data were collected in a psychology labora-
tory and in classrooms. Due to significant missing data,
three questionnaires were excluded from statistical anal-
ysis. We also excluded individuals with invalid BRIEF-A
protocols (n=2) and those who were more than 30 years
of age (n=26) to allow for a similar young adult age
profile, resulting in the final sample of 212 individuals.
Measures and scoring
Participants first completed a demographic question-
naire, which asked them to report their age (in years),
sex (male or female), and race (African American,
Asian, Caucasian, Hispanic/Latino, Native American,
or “Other”). Participants also reported any medi-
cal or psychiatric conditions (scored dichotomously)
including: diagnosis of learning disability, diagnosis of
attention-deficit/hyperactivity disorder (ADHD), cur-
rent use of alcohol, diagnosis of psychiatric/neurological
illness(es), diagnosis of chronic/major medical prob-
lem(s), and current illicit drug use. Participants then
completed the following study measures in the order in
which they are listed: (a) Lay General Procrastination
(GP) Scale, Student Version (Lay, 1986); (b) Beck
Depression Inventory–II (BDI–II, Beck, Steer, & Brown,
1996); (c) Beck Anxiety Inventory (BAI; Beck & Steer,
1993), Shipley Institute of Living Scale (Shipley, 1991),
BRIEF-A, and NEO Five Factor Inventory (NEO-FFI;
Costa & McRae, 1992).
The Lay GP is a 20-item measure of trait procras-
tination that examines behavioral tendencies to delay
the start or completion of everyday tasks. Participants
rate various statements on a 5-point Likert scale
(1 =extremely uncharacteristic; 5 =extremely charac-
teristic). Sample items include: “I often find myself per-
forming tasks that I had intended to do days before”
and “I usually start an assignment shortly after it is
assigned.” Ten items are reverse-keyed, and scores range
from 20 to 100 with a higher total score indicating greater
procrastination. The Lay GP is considered unidimen-
sional, and it has good validity and reliability in a variety
of contexts (Diaz-Morales, Ferrari, Diaz, & Argumedo,
2006; Ferrari, 1989; 1991b; Lay, 1988; Lay & Burns,
1991).
The BRIEF-A is a self-report measure of executive
functions or self-regulation in the everyday environment,
which includes nine nonoverlapping theoretically and
empirically derived clinical scales. Participants rate the
frequency of 75 problematic behaviors over the past
month on a 3-point scale (1 =never; 2 =sometimes;
3=often), and higher scores indicate greater degrees
of executive dysfunction. Mean raw scores and stan-
dard Tscores can be calculated for each of the clinical
scales, and there are also three validity scales (Negativity,
Inconsistency, and Infrequency); as mentioned above,
we excluded participants with elevated scores on one
or more of the validity scales (defined as a Tscore of
65 or greater). The BRIEF-A has demonstrated reli-
ability, validity, and clinical utility as an ecologically
sensitive measure of executive functioning in healthy
individuals and also those presenting with a range of
psychiatric and neurological conditions (Roth et al.,
2005).
The BRIEF-A Inhibit scale contains 8 items that mea-
sure behavioral regulation or the ability to not act on
an impulse (e.g., “I have problems waiting my turn”).
The Self-Monitor scale contains 6 items that measure the
extent to which a person keeps track of his/her behav-
ior and its impact on others (e.g., “When people seem
upset with me, I don’t understand why”; “I say things
without thinking”). The Plan/Organize scale contains 10
items that assess the ability to manage current and future-
oriented task demands within their situational contexts
(e.g., “I don’t plan ahead for tasks”; “I have trouble
organizing work”), The Shift scale contains 6 items that
measure the ability to shift behaviorally or cognitively
from one situation, activity, or aspect of a problem to
another, as the circumstances demand (e.g., “I have trou-
ble thinking of a different way to solve a problem when
stuck”). The Initiate scale contains 8 items related to
the ability to begin a task and to independently gener-
ate ideas, responses, or problem-solving strategies (e.g.,
“I start things at the last minute such as assignments,
chores, tasks”). The Task Monitor scale contains 6 items
that measure the extent to which an individual keeps
track of his/her problem-solving success or failure (e.g.,
“I misjudge how difficult or easy tasks will be”). The
Emotional Control scale contains 10 items related to a
person’s ability to modulate emotional responses (e.g., “I
overreact to small problems”; “I get emotionally upset
easily”). The Working Memory scale contains 10 items
that tap the capacity to hold information in mind for the
purpose of generating a response or completing a task
(e.g., “I have trouble with jobs or tasks that have more
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EXECUTIVE DYSFUNCTION AND ACADEMIC PROCRASTINATION 347
than one step”). The Organization of Materials scale has
8 items that assess orderliness in one’s everyday environ-
ment and the ability to keep track of everyday objects,
including homework (e.g., “I have trouble finding things
in my room, closet, or desk”).
The NEO-FFI is a used to measure the “Big Five”
domains of adult personality (openness, conscientious-
ness, extraversion, agreeableness, and neuroticism). The
60 items include questions about typical behaviors
or reactions. Participants rate themselves using a 5-
point Likert scale (1 =strongly disagree; 5 =strongly
agree). The current study analyzed participants’ scores
on the neuroticism and conscientiousness subscales
because of their known association with procrastina-
tion. Neuroticism refers to a person’s stress reactivity
or emotional responsiveness to challenge and procliv-
ity for negative mood states such as anxiety or worry.
Conscientiousness denotes the extent to which a person
is task oriented, achievement striving, deliberate, depend-
able, careful, and organized, and possesses self-control.
We utilized NEO-FFI raw scores (range 0 to 48 per
scale), which can be used to derive percentiles from a
college-age normative data sample. Previous research has
demonstrated the reliability and validity of this measure
(Caruso, 2000; Costa & McCrae, 1992; Holden, 1992)
and its association with a variety of psychological and
health variables (John & Srivastava, 1999; Matthews,
Deary, & Whiteman, 2003).
The Shipley Institute of Living Scale (Shipley) is a test
of general intellectual functioning for adults and ado-
lescents, which contains two subscales—Vocabulary and
Abstraction. The 40-item Vocabulary section requires
individuals to select the word closest in meaning to a
target word from among four alternatives. The 20-item
Abstraction section requires individuals to complete a
series of numbers, letters, or words with the next item
that should follow in the sequence. A total score is
calculated, which is used to derive a Full Scale IQ
estimate. The Shipley has shown strong psychomet-
ric properties in both healthy and clinical populations
(Nixon, Parsons, Schaeffer, & Hale, 1995; Phay, 1990;
Smith & McCrady, 1991; Zachary, Crumpton, & Spiegel,
1985).
The Beck Depression Inventory–II (BDI–II; Beck
et al., 1996) and Beck Anxiety Inventory (BAI; Beck
& Steer, 1993) are among the most widely used self-
administered measures of emotional functioning, and
there is strong support for their reliability and validity
with young adults (Arnou, Meagher, Norris, & Branson,
2001; Carmody, 2005; Osman, Kopper, Barrios, Osman,
& Wade, 1997). The BDI–II consists of 21 items that
assess the intensity of depression experienced in the past
two weeks. Each item contains a list of four statements
arranged in increasing severity about a particular symp-
tom; total scores range from 0 to 63, with higher scores
indicating stronger severity of depressive symptoms. The
BAI evaluates both physiological and cognitive symp-
toms of anxiety and consists of 21 self-administered
items, each describing a common symptom. The BAI
is rated on a scale of 0 to 3 (0 =not at all bothered;
3=I could barely stand it), indicating the degree to
which the individual has been bothered by each symp-
tom during the past week. The BAI has been found to
reliably discriminate anxiety from depression while dis-
playing convergent validity (Beck & Steer). Item scores
are summed to obtain a total score that can range from
0 to 63, with higher scores indicating higher levels of
anxiety.
Statistical analyses
Descriptive statistics were calculated for all variables. For
the multivariate analyses, the continuous variable of aca-
demic procrastination was used as the outcome variable.
Linear regression analysis was used to determine the vari-
ables associated with the outcome of academic procrasti-
nation. Two models were conducted for each of the nine
BRIEF-A executive functioning clinical scales. Model 1
consisted of the independent variables of demographic
variables (i.e., age, sex, race/ethnicity dichotomized as
Caucasian versus minority), presence of a number of
medical and psychiatric diseases or conditions (i.e., diag-
nosis of learning disability, diagnosis of ADHD, cur-
rent use of alcohol, diagnosis of psychiatric/neurological
illness(es), diagnosis of chronic/major medical prob-
lem(s), and current illicit drug use), and the particu-
lar executive functioning category. Model 2 consisted
of the independent variables of those in Model 1 plus
estimated IQ, depressive symptoms, anxiety symptoms,
NEO-Neuroticism, and NEO-Conscientiousness. SPSS
Version 17 was used for all analyses. Analyses in Model 2
had 24 fewer participants because of missing data on the
BDI–II.
RESULTS
Table 1 shows the descriptive statistics. The sample had
an average age of more than 21 years, and more than three
quarters were women. In terms of ethnic/racial com-
position, 60% identified as Caucasian, 14% Asian, 13%
African American, 5% Hispanic, and 8% as “Other.” For
the purposes of the statistical analyses, race was treated
as a dichotomous variable (i.e., Caucasian/minority)
in order to prevent possible statistical overadjustment.
Almost half the sample reported drinking alcohol, with
an average of 2.7 drinks per week (SD =2.9) among
those who drank. The other five conditions/diseases
all were less than 5%. Though not shown in Table 1,
the psychiatric/neurological illnesses included border-
line personality disorder, obsessive compulsive disorder,
generalized anxiety disorder, various eating disorders,
depression, epilepsy, and traumatic brain injury. The
reported medical conditions included asthma, hyperthy-
roidism, diabetes, cardiovascular disease, and polycys-
tic ovary disease. Average procrastination scores were
within the neutral range, while scores ranged from min-
imal to severe. Average BRIEF-A scores were within
the normal range, while approximately 12.5% of scores
were within the clinically elevated range (defined as a T
score of 65 or greater). Average NEO Neuroticism and
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348 RABIN, FOGEL, NUTTER-UPHAM
TABLE 1
Descriptive statistics for the sample of 212 undergraduate
students
Variables Mean (SD) % (n)
Age (years) 21.7 (2.65)
Gender
Male 23.1 (49)
Female 76.9 (163)
Race
Caucasian 59.9 (127)
Minority 40.1 (85)
Learning disability
No 96.7 (205)
Yes 3.3 (7)
ADHD
No 98.1 (208)
Yes 1.9 (4)
Alcohol drinking
No 50.5 (107)
Yes 49.5 (105)
Psychiatric/neurological illness
No 95.8 (203)
Yes 4.2 (9)
Major medical problems
No 95.3 (202)
Yes 4.7 (10)
Drug use
No 95.3 (202)
Yes 4.7 (10)
Procrastination 60.6 (9.09)
BRIEF-A
Inhibit 12.6 (2.87)
Shift 9.8 (2.37)
Emotional Control 17.3 (4.47)
Self-Monitor 9.2 (2.35)
Initiate 12.9 (2.91)
Working Memory 12.3 (2.95)
Plan/Organize 15.5 (3.47)
Task Monitor 10.2 (2.06)
Organization of Materials 12.8 (3.53)
BAI 10.8 (9.65)
BDI–II (n=192) 10.1(8.44)
Shipley IQ 112.6 (7.50)
NEO-N (n=210) 28.2 (8.00)
NEO-C (n=210) 35.4 (7.57)
Note. SD =standard deviation, ADHD =attention-deficit/
hyperactivity disorder, BRIEF-A =Behavior Rating Inventory
of Executive Function–Adult Version, BAI =Beck Anxiety
Inventory, BDI–II =Beck Depression Inventory–Version 2, IQ
=intelligence quotient, NEO-N =NEO Neuroticism, NEO-C
=NEO Conscientiousness.
Conscientiousness scores were within the average to high
range, with scores that ranged from low to high. Average
anxiety and depressive symptoms were within the mini-
mal to mild range, with scores that ranged from minimal
to severe.
Table 2 shows the linear regression analyses for the
executive functioning categories of Inhibit, Shift, and
Emotional Control. For Inhibit, Model 1 shows signif-
icance for both increasing age and Inhibit with increas-
ing procrastination. Model 2 shows similar significant
results for age and Inhibit. Also, NEO-Conscientiousness
approached significance (p=.054) with decreasing scores
associated with increasing procrastination. For Shift,
Model 1 shows significance for both increasing age
and Shift with increasing procrastination, and increasing
BDI–II had a p-value of .098 for association with increas-
ing procrastination. Model 2 shows significance for
increasing age and decreasing NEO-Conscientiousness
scores associated with increasing procrastination while
Shift was no longer significantly associated with pro-
crastination. For Emotional Control, Model 1 shows
significance for both increasing age and Emotional
Control with increasing procrastination. Model 2 shows
significance for increasing age and decreasing NEO-
Conscientiousness scores associated with increasing pro-
crastination while Emotional Control was no longer
significantly associated with procrastination.
Table 3 shows the linear regression analyses for the
executive functioning categories of Self-Monitor, Initiate,
and Working Memory. For Self-Monitor, Model 1 shows
significance for both increasing age and Self-Monitor
with increasing procrastination. Model 2 shows simi-
lar significant results for age and Self-Monitor. Also,
decreasing NEO-Conscientiousness scores were signif-
icantly associated with increasing procrastination, and
increasing BDI–II had a p-value of .098 for associa-
tion with increasing procrastination. For Initiate, Model
1 shows significance for Initiate with increasing pro-
crastination. Model 2 shows similar significant results
for Initiate. Also, increasing age approached signifi-
cance (p=.061) with increasing procrastination. For
Working Memory, Model 1 shows significance for both
increasing age and Working Memory with increasing pro-
crastination. Model 2 shows similar significant results
for age and Working Memory. Also, decreasing NEO-
Conscientiousness scores were significantly associated
with increasing procrastination.
Table 4 shows the linear regression analyses for the
executive functioning categories of Plan/Organize,
Task Monitor, and Organization of Materials.
For Plan/Organize, Model 1 shows significance for
Plan/Organize with increasing procrastination. Model 2
shows similar significant results for Plan/Organize. Also,
those without learning disabilities approached signifi-
cance (p=.050) for increasing procrastination. For Task
Monitor, Model 1 shows significance for Task Monitor
with increasing procrastination. Also, increasing age
approached significance (p=.055) with increasing
procrastination. Model 2 shows similar significant
results for Task Monitor, age now was also significantly
associated, and decreasing NEO-Conscientiousness
scores approached significance (p=.088) with increasing
procrastination. For Organization of Materials, Model
1 shows significance for Organization of Materials with
increasing procrastination. Also, increasing age had a
p-value of .09 for association with increasing procras-
tination. Model 2 shows similar significant results for
Organization of Materials, increasing age approached
significance (p=.057) with increasing procrastina-
tion, and increasing BDI–II had a p-value of .089 for
association with increasing procrastination.
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TABLE 2
Linear regression analyses for predictors of executive functioning categories of inhibit, shift, and emotional control
Inhibit Model 1 Inhibit Model 2 Shift Model 1 Shift Model 2 EMOT Model 1 EMOT Model 2
Variables B (n =212) SE B (n =188) SE B (n =212) SE B (n =188) SE B (n =212) SE B (n =188) SE
Constant 32.95 6.23∗∗∗ 28.89 13.7039.99 5.97∗∗∗ 39.216 13.61∗∗ 43.04 5.87∗∗∗ 43.28 13.30∗∗
Age 0.59 0.240.62 0.260.50 0.250.56 0.270.49 0.250.56 0.27
Female 1.82 1.44 1.20 1.62 1.22 1.48 0.65 1.64 0.72 1.54 0.49 1.67
Race M 0.16 1.27 0.19 1.36 0.43 1.29 0.39 1.39 0.12 1.33 0.36 1.42
LD 1.84 3.76 4.07 4.18 3.73 3.85 5.42 4.25 3.13 3.89 5.34 4.29
ADHD 4.53 4.80 0.49 5.93 2.32 4.89 1.77 6.02 1.24 4.94 2.32 6.03
ALCH 2.02 1.29 2.22 1.40 0.94 1.31 1.42 1.42 1.25 1.32 1.55 1.42
PSYCH/NEUR 0.03 3.08v 1.52 3.52 1.35 3.13 0.16 3.55 1.76 3.16 0.22 3.57
MED PROB 2.62 2.85 2.46 2.91 3.87 2.91 3.27 2.97 4.08 2.95 3.26 2.99
DRUG 0.27 2.95 0.43 3.43 1.13 3.03 0.68 3.54 0.83 3.06 0.22 3.55
Inhibit 1.01 0.22∗∗∗ 0.83 0.26∗∗ — — — — — — — —
Shift 0.85 0.26∗∗ 0.44 0.32 — — — —
Emotional Control — — — — — — — — 0.35 0.150.09 0.19
IQ 0.09 0.10 — — 0.07 0.10 — 0.06 0.10
BAI 0.03 0.08 — — 0.06 0.08 — 0.07 0.08
BDI–II 0.17 0.10#— — 0.13 0.11 — — 0.15 0.11
NEO-N 0.08 0.10 — — 0.13 0.10 — 0.15 0.10
NEO-C 0.20 0.10#——0.25 0.11——0.27 0.11
Note. B =beta; M =minority; LD =learning disability; ADHD =attention deficit hyperactivity disorder; ALCH =alcohol drinking; PSYCH/NEUR =psychiatric/neurological illness; MED
PROB =major medical problems; DRUG =drug use; IQ =intelligence quotient; BAI =Beck Anxiety Inventory; BDI–II =Beck Depression Inventory–Version 2; NEO-N =NEO Neuroticism;
NEO-C =NEO Conscientiousness; EMOT =emotional control. Standard errors in parentheses.
#p<.10. p<.05. ∗∗p<.01. ∗∗∗p<.001.
349
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TABLE 3
Linear regression analyses for predictors of executive functioning categories of self-monitor, initiate, and working memory
Self-Monitor Model 1 Self-Monitor Model 2 Initiate Model 1 Initiate Model 2
Working Memory Model
1
Working Memory Model
2
Variables B (n =212) SE B (n =188) SE B (n =212) SE B (n =188) SE B (n =212) SE B (n =188) SE
Constant 37.91 6.14∗∗∗ 34.85 13.5234.26 5.49∗∗∗ 34.54 12.83∗∗ 36.23 5.88∗∗∗ 31.26 13.55
Age 0.618 0.250.65 0.270.39 0.23 0.48 0.26#0.54 0.240.58 0.26
Female 1.13 1.48 0.28 1.63 1.31 1.38 1.09 1.58 1.31 1.44 0.86 1.61
Race M 0.45 1.29 0.40 1.38 1.09 1.21 1.19 1.35 0.92 1.26 0.77 1.37
LD 3.95 3.84 6.18 4.21 4.72 3.60 6.16 4.08 4.73 3.77 6.11 4.170
ADHD 0.88 4.87 2.77 5.93 1.42 4.56 1.14 5.77 2.33 4.77 1.98 5.88
ALCH 1.46 1.31 1.75 1.40 1.20 1.22 1.71 1.36 1.45 1.28 1.82 1.39
PSYCH/NEUR 1.65 3.09 0.59 3.53 1.02 2.89 0.56 3.41 1.04 3.04 0.36 3.49
MED PROB 3.36 2.90 3.03 2.93 3.45 2.72 3.12 2.85 2.71 2.85 2.47 2.92
DRUG 0.59 3.01 0.59 3.48 0.15 2.82 0.55 3.37 0.82 2.95 0.81 3.45
Self-Monitor 0.92 0.27∗∗ 0.71 0.29— — — — — — —
Initiate 1.29 0.20∗∗∗ 1.15 0.28∗∗∗ — — — —
Working Memory — — — — — 0.95 0.21∗∗∗ 0.73 0.24∗∗
IQ 0.07 0.10 0.02 0.10 0.10 0.10
BAI 0.05 0.08 0.01 0.08 0.06 0.08
BDI–II 0.17 0.11# 0.08 0.10 0.11 0.11
NEO-N 0.14 0.10 0.04 0.10 0.11 0.10
NEO-C 0.23 0.10——0.11 0.11 0.23 0.10
Note. B =beta; M =minority; LD =learning disability; ADHD =attention deficit hyperactivity disorder; ALCH =alcohol drinking; PSYCH/NEUR =psychiatric/neurological illness; MED
PROB =major medical problems; DRUG =drug use; IQ =intelligence quotient; BAI =Beck Anxiety Inventory; BDI–II =Beck Depression Inventory–Version 2; NEO-N =NEO Neuroticism;
NEO-C =NEO Conscientiousness. Standard errors in parentheses.
#p<.10. p<.05. ∗∗p<.01. ∗∗∗p<.001.
350
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TABLE 4
Linear regression analyses for predictors of executive functioning categories of plan/organize, task monitor, and organization of materials
Plan/Organize
Model 1 Plan/Organize Model 2
Task Monitor
Model 1 Task Monitor Model 2 ORG MAT Model 1 ORG MAT Model 2
Variables B (n =212) SE B (n =188) SE B (n =212) SE B (n =188) SE B (n =212) SE B (n =188) SE
Constant 33.146 5.43∗∗∗ 23.38 12.83#31.38 5.94∗∗∗ 26.15 13.49#38.90 5.53∗∗∗ 26.08 13.83#
Age 0.35 0.23 0.41 0.25 0.45 0.24#0.52 0.260.41 0.24#0.50 0.26#
Female 1.55 1.36 1.43 1.54 2.04 1.41 1.309 1.59 1.55 1.42 1.07 1.60
Race M 0.64 1.19 0.86 1.30 0.46 1.23 0.41 1.34 0.68 1.25 0.50 1.36
LD 5.85 3.56 7.87 3.98#3.00 3.66 4.91 4.10 4.37 3.72 6.62 4.15
ADHD 0.38 4.51 2.25 5.58 1.23 4.65 1.92 5.79 0.07 4.72 3.47 5.85
ALCH 0.94 1.20 1.15 1.32 0.96 1.25 1.38 1.36 1.38 1.26 1.65 1.38
PSYCH/NEUR 1.32 2.84 0.252 3.31 1.26 2.95 0.34 3.43 1.76 2.98 0.81 3.47
MED PROB 2.22 2.69 1.75 2.77 2.43 2.78 2.27 2.87 2.00 2.83 1.67 2.92
DRUG 0.65 2.78 1.53 3.28 0.62 2.88 1.17 3.40 0.19 2.91 0.99 3.43
Plan/Organize 1.15 0.17∗∗∗ 1.21 0.23∗∗∗ — — — — — —
Task Monitor 1.62 0.29∗∗∗ 1.29 0.33∗∗∗ — — —
Organization of Materials — — — — — 0.87 0.17∗∗∗ 0.77 0.22∗∗
IQ — — 0.08 0.09 — — 0.09 0.10 — — 0.13 0.10
BAI — — 0.03 0.08 — — 0.05 0.08 — — 0.07 0.08
BDI–II — — 0.13 0.11 — — 0.13 0.10 — — 0.18 0.10#
NEO-N 0.05 0.10 0.09 0.109 0.04 0.10
NEO-C 0.03 0.11 0.18 0.10#——0.12 0.11
Note. B =beta; M =minority; LD =learning disability; ADHD =attention-deficit/hyperactivity disorder; ALCH =alcohol drinking; PSYCH/NEUR =
psychiatric/neurological illness; MED PROB =major medical problems; DRUG =drug use; IQ =intelligence quotient; BAI =Beck Anxiety Inventory; BDI–II =Beck
Depression Inventory–Version 2; NEO-N =NEO Neuroticism; NEO-C =NEO Conscientiousness; ORG MAT =organization of materials. Standard errors in parentheses.
#p<.10. p<.05. ∗∗p<.01. ∗∗∗p<.001.
351
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352 RABIN, FOGEL, NUTTER-UPHAM
DISCUSSION
A substantial body of research reveals the prevalence
of academic procrastination as a self-perceived problem
for college students, with consequences ranging from
reduced academic achievement and progress to increased
stress and poor quality of life. To our knowledge, no
research has investigated the subcomponents of self-
reported executive functioning most related to procras-
tination in undergraduate students despite the obvious
overlap between these constructs. In a diverse sample
of college undergraduates aged 30 years and below, we
found that all nine clinical subscales of executive func-
tioning were significantly associated with increasing aca-
demic procrastination in a model that included personal
and medical demographic characteristics. Also, in most
of the analyses increasing age showed a significant or
trend level association with increasing procrastination. In
a second model that also included various demographics
and psychological variables (i.e., intellectual, personality,
mood), lower conscientiousness also showed a significant
or trend-level association with increasing procrastination
in the majority of analyses.
As hypothesized, the findings showed the importance
of various aspects of self-reported executive functions in
predicting a tendency toward academic procrastination
in college students. In the second model, which included
all the demographic, medical, and psychological vari-
ables, the BRIEF-A subscales of Initiate, Plan/Organize,
Organization of Materials were independently associated
with academic procrastination. Close attention to the
abilities tapped by these subscales is warranted with an
eye towards implications for remediation of problematic
delay behaviors. Individuals with initiation problems typ-
ically want to succeed but have difficulty getting started
and may require extensive prompts or cues to begin an
activity. Those with planning and organization difficul-
ties may fail to begin academic tasks in a timely fashion
or fail to function efficiently because they do not have
required objects or materials available when they finally
sit down to work. They also may approach tasks in a hap-
hazard fashion or become overwhelmed by large amounts
of information (Roth et al., 2005).
While it is not surprising that initiation, planning, and
organizational skills were predictive of academic pro-
crastination, these results speak to the importance of
working with students to improve these abilities. Effective
strategies may involve teaching students to set proximal
subgoals for their academic work along with reason-
able expectations about the amount of effort required
to complete a given task (Ariely & Wertenbroch, 2002;
Brooke & Ruthven, 1984; Lamwers & Jazwinski, 1989;
Tuckman, 1998). The use of contracts for periodic work
completion, administration of weekly or repeated quizzes
until topic mastery is achieved, and development of
short assignments that build on one another with regu-
lar deadlines and feedback are helpful strategies. These
goal-setting and achievement experiences enable stu-
dents with procrastination tendencies to try out what
it is like to complete assignments on time (Ackerman
& Gross, 2005; Seo, 2008). They also serve to enhance
self-efficacy and self-satisfaction with performance and
may diminish the perceived burden or aversiveness asso-
ciated with task completion (Stock & Cervone, 1990). In
turn, procrastination may be reduced as deadlines seem
less distant and the intention–action gap narrows (Steel,
2007).
Four other BRIEF-A clinical scales were significant
predictors of procrastination—those of Inhibit, Self-
Monitor, Working Memory, and Task Monitor. The
Inhibit and Self-Monitor subscales measure the abil-
ity to not act on impulse and to keep track of and
maintain appropriate regulatory control over behavior.
It is widely known that procrastinators tend to choose
short-term benefits over long-term gains, reflecting a core
component of poor self-regulation (Tice & Baumeister,
1997). How then can students learn to overcome their
natural preference for impulsive gratification through
self-control and engagement in behaviors that facilitate
attainment of long-term academic goals? Pychyl et al.
(2000a) draw upon previous research by Ainslie and
Haslam (1992) and suggest that students who procras-
tinate are seeking temporary relief from the negative
or anxious affect associated with unpleasant academic
tasks. Through counseling, such individuals should be
less inclined towards this short-term affective improve-
ment, which comes at the expense of long-term goal
attainment and self-management. In working with such
students, a first step might be to help them develop an
awareness of these emotions and their role in jeopardiz-
ing achievement. Subsequently, students are trained in
volitional skills (i.e., how to maintain action against com-
peting goal tendencies while managing intrusive effects
of negative affect). Related competencies include gaining
control over immediate impulses through the establish-
ment of fixed daily routines for learning and leisure
activities, task persistence, and time management (Dietz,
Hofer, & Fries, 2007). In addition, the skills necessary to
initiate a task often need to be isolated and broken into
small, attainable steps. Students can learn to be mind-
ful of the resources required to carry out these steps and
identify and troubleshoot problems that arise while draw-
ing on both working memory and task monitoring skills
(Haycock et al., 1998).
Procrastination is conceptually and empirically linked
to conscientiousness, a trait reflecting responsibility, self-
discipline, achievement motivation, and the careful and
diligent fulfillment of obligations. In addition, consci-
entiousness shows an increase in young adulthood cor-
responding to the maturation of frontal brain regions
that subserve various executive functions (Robins, Fraley,
Roberts, & Trzesniewski, 2001; Welsh, Pennington, &
Groisser, 1991). Our results indicated that low consci-
entiousness was an important predictor of procrastina-
tion, and it overrode the significance of several com-
ponents of executive functioning—namely, the ability
to shift from one situation or activity to another and
to modulate emotional responses. While it might be
tempting to target this characteristic directly, personal-
ity traits are relatively stable and enduring, and not easily
modifiable. Nonetheless, some have suggested that
the negative effects of low conscientiousness can be
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EXECUTIVE DYSFUNCTION AND ACADEMIC PROCRASTINATION 353
ameliorated through techniques focused on organiza-
tion and the stimulation of self-control. An easy way to
increase self-control and prevent distraction is to block
access to short-term temptations—for example, by train-
ing oneself to study in a library, work with a clean desk-
top, and/or study with the door closed (Schouwenburg,
2004). Others have highlighted the value of achievement
motivation, which can be enhanced by setting more diffi-
cult academic goals and learning to enjoy performance
for its own sake, reducing the aversive nature of due
tasks (Costa & McCrae, 1992; Spence & Helmreich,
1983). Peer monitoring with accountability and conse-
quences for behavioral failure and self-appraisal methods
(e.g., self-tests with criteria for mastery included) also
may improve academic conscientiousness (Tuckman &
Schouwenburg, 2004). Questions remain, however, about
the long-lasting or sustainable nature of the behavior
change with these interventions. Furthermore, conscien-
tiousness may be negatively related with flexibility and
creativity (Feist, 1998; van Eerde, 2003; Wolfradt & Pretz,
2001), which may be problematic in academic situations
that require innovative solutions.
Only a few other predictors yielded significant or
trend-level findings. First, even within this restricted
sample of undergraduates, increasing age showed a con-
sistently strong association with higher levels of pro-
crastination. This goes against most reported findings
(Haycock et al., 1998; Howell et al., 2006; Steel, 2007;
van Eerde, 2003), but may reflect the previous finding that
academic procrastination increases as students advance
through the educational process (Rosário et al., 2009),
with college seniors procrastinating more than first-year
students (Hill, Hill, Chabot, & Barrall, 1978; McCown &
Roberts, 1994, as cited in Ozer et al., 2009). Perhaps the
longer a student remains in school, the less enthusiastic
and motivated he/she becomes or the more entrenched
bad habits become. It is also possible that familial and
work responsibilities increasingly limit the time one can
devote to academic tasks, or students may acquire addi-
tional bad academic habits over time. These possibilities,
however, need to be further explored empirically. What is
clear is that older undergraduate students in the current
study were at greater risk for academic procrastination
and possibly represent an at-risk group for negative con-
sequences based on dilatory behaviors.
Second, although depression has several characteristics
that make it a likely suspect for causing procrastina-
tion (e.g., low energy, poor concentration), we saw only
minimal, trend-level evidence for the association between
increasing depressive symptoms and greater procrastina-
tion. This is consistent with recent meta-analytic findings,
which concluded that depression’s connection appears to
be due mostly to waning energy levels, which makes tasks
more aversive to pursue (Steel, 2007). Additionally, as
noted by Pychyl et al. (2000a), when students are procras-
tinating, they may not concurrently experience negative
affect because they are engaged in pleasant activities to
the neglect of those found more aversive. In summariz-
ing the effects of mood on procrastination, van Eerde
(2003) observed that mood variables are just as likely to
be precursors as outcomes of procrastination, and extant
research provides no indication of whether to consider
them as antecedents or consequences. Clearly the link
between procrastination and depression is complex, and
future research might be useful to further understand this
relationship. Third, there was one model in which pres-
ence of a learning disability was associated with lower
procrastination, perhaps due to the tendency of this stu-
dent population to seek out academic help or remediation
(Trainin & Swanson, 2005). This finding is in contrast
to the one previously published study (Klassen, 2008a),
which found that college students with learning disabil-
ities reported higher levels of procrastination than their
peers and lower levels of metacognitive self-regulation
and self-efficacy for self-regulation. It is unclear what
accounts for this discrepancy, but it is important to note
that both the depression and learning disability findings
were at trend levels and need to be replicated and clarified
before conclusions can be drawn.
Contrary to our hypotheses, we did not find signif-
icance for the predictors of neuroticism and anxiety,
two closely related traits. This is consistent with results
of a recent meta-analyses of procrastination’s possible
causes and effects (Steel, 2007), which suggested that
neuroticism’s connection to procrastination was primar-
ily due to impulsiveness and added little unique vari-
ance over conscientiousness (Johnson & Bloom, 1995;
Lee et al., 2006; Schouwenburg & Lay, 1995). Similarly,
Haycock et al. (1998) found that anxiety did not con-
tribute significantly to the variance in procrastination and
concluded that anxiety should be examined and inter-
preted in the context of its relationship to other variables.
Steel also noted that moods are prone to change, and that
procrastinators may feel remorse for their inactions at
any time, perhaps after the academic semester has ended.
Consequently, researchers might need to test mood at
more than one time point or over longer time periods,
in order to detect a relationship with procrastination.
Research employing repeated measures of state anxiety
over an academic semester supports the idea that procras-
tinators tend to experience less stress early on, but more
stress later on and overall (Tice & Baumeister, 1997).
Additional support comes from research that found a
relationship between procrastination and anxiety but
only as an increase during the last week of the course
(Lay & Schouwenburg, 1993) or as a decrease at the
course beginning (Towers & Flett, 2004, as cited by Steel,
2007). Clearly, the relationships between procrastination
and neuroticism/mood are complex and may not be best
described in a general linear fashion.
Limitations
Our findings, while suggestive that aspects of self-
reported executive dysfunction are related to academic
procrastination, warrant consideration in the context
of study limitations. Despite its advantage as a com-
prehensive, ecologically sensitive measure of executive
functioning, the BRIEF-A, along with all self-report
instruments, is open to the criticism that it may have pro-
duced socially desirable responses or other biases. The
Downloaded By: [Rabin, Laura A.] At: 20:57 3 March 2011
354 RABIN, FOGEL, NUTTER-UPHAM
present findings are therefore preliminary and would be
strengthened if future research were to use behavioral
measures of task postponement in addition to self-report
instruments (e.g., Howell et al., 2006; Milgram, Dangour,
& Raviv, 1992). External correlates of executive function
are also needed in the form of clinical neuropsycho-
logical measures. Seven of the nine BRIEF-A subscales
showed a significant association with procrastination,
and while we achieved ample sensitivity, specificity could
be improved. To this end, objective neuropsychologi-
cal instruments should be selected that assess domains
of executive functioning indentified as important in the
current study with the goal of improving our speci-
ficity and further delineating the relationship between
academic procrastination and various executive func-
tions.
Given the overrepresentation of females in social sci-
ence participant pools as well as data collection time
constraints, we could not recruit more males, though this
would have been preferable. We attempted to address this
sampling limitation by using gender and other demo-
graphic characteristics as covariates in all analyses, with
no significant findings pertaining to these variables. A
methodological limitation is that we did not counterbal-
ance the ordering of tests, though we did separate the
Lay from the BRIEF-A by placing one at the begin-
ning and one at the end of the test battery. Because
the Lay and BRIEF-A tap similar behaviors and cog-
nitive styles, it might have been preferable to counter-
balance to minimize the possibility that participants’
responses were influenced by ordering of critical ques-
tions. This study was correlational and cross-sectional
in nature, and we therefore cannot draw conclusions
pertaining to directionality or predictive value of exec-
utive function difficulties to long-term procrastination
and associated negative outcomes. While it is plausi-
ble that procrastinators lack executive control skills,
it is also possible that both procrastination and exec-
utive dysfunction are caused by another variable or
variables, and the current study was not designed to
address this possibility. Similarly, it is possible that
BRIEF-A scores served as a proxy for either diagnosed
or undiagnosed ADHD, which by definition leads to
these symptoms. However, 2% of our sample reported
a diagnosis of ADHD, and the prevalence in adults is
about 4% (Kessler et al., 2006). Thus, due to under-
reporting or underdiagnosis, we may have overlooked
another 4–6 individuals, which would not have altered
our overall pattern of findings. Furthermore, the current
study was intended to investigate the relation between
mild executive dysfunction and procrastination in a gen-
erally healthy adult sample rather than in a clinical
sample for whom the pattern and overall severity of
BRIEF-A scores would likely differ. Finally, we mea-
sured various aspects of task postponement but did
not inquire directly about the adverse impact of such
deferment on functioning. An increased understanding
of the antecedents, motivational dynamics, and effects
of procrastination will help to identify the appropriate
strategies to remediate the problematic aspects of this
behavior.
Future directions
Our findings are consistent with the conceptualization of
executive functioning as central to the ability to engage
in independent, goal-oriented behavior, especially in the
context of unstructured, novel, or complex tasks, and
suggest that procrastination could be an expression of
subtle executive dysfunction—even in this group of neu-
ropsychologically healthy young adults. Executive func-
tions rely on a number of cortical and subcortical brain
regions including prefrontal cortices, anterior cingulate
gyrus, basal ganglia and diencephalic structures, cerebel-
lum, deep white matter tracks, and parietal lobes areas.
These brain areas are richly interconnected and are also
linked with many additional regions that together sub-
serve virtually all cognitive processes (Funahashi, 2001;
Greene, Braet, Johnson, & Bellgrove, 2008; Roth et al.,
2006). While executive dysfunction is observed in vari-
ous psychiatric, neurological, and systemic disorders, our
research suggests that there may be problems within cog-
nitively healthy individuals that contribute to a vulnera-
bility to procrastination. Future research might identify
subtle neuroanatomic or functional brain abnormalities
associated with procrastination. As training of cognitive
strategies has been found to alter brain activity or neuro-
chemistry (Olesen, Westerberg, & Klingberg, 2004; Roth
et al., 2006; Valenzuela et al., 2003), pre–post interven-
tion studies using neuroimaging paradigms might also
provide evidence of neurobiological mediation of pro-
crastination.
Researchers are also exploring the degree to which
individual differences in executive capacity may be
attributed, at least in part, to genetic variation (Goldberg
& Weinberger, 2004; Kempf & Meyer-Lindenberg, 2006).
Individual differences in executive function are being con-
sidered at multiple levels of analysis, including potential
genotypes, proposed endophenotypes (e.g., performance
on cognitive tasks that involve executive functions), and
relevant phenotypes associated with executive function-
ing, such as temperament, personality, and psychopathol-
ogy (Williams et al., 2009). If feasible, the combination of
neuroimaging techniques with behavioral measures, self
report, and genetics may help refine the phenotype of
procrastination and inform the development of strate-
gic individualized treatments. It will also be important
to validate self-reported procrastination with external
measures such as grade point average, such as num-
ber of missing or late assignments, incomplete grades,
and so on.
Our results revealed increased age as a significant pre-
dictor of academic procrastination, which suggests the
need to target at-risk upper level students who may be
struggling to remain productive. The additional find-
ing of low conscientiousness among procrastinating stu-
dents is consistent with their characterization as less
self-regulated and disciplined and suggests avenues for
remediation (described above). Such interventions should
account for a recent finding, which demonstrates that
in addition to self-regulation skills, students must also
possess the confidence to implement effective learning
strategies, resist distractions, complete schoolwork, and
Downloaded By: [Rabin, Laura A.] At: 20:57 3 March 2011
EXECUTIVE DYSFUNCTION AND ACADEMIC PROCRASTINATION 355
participate in class learning (referred to as “self-efficacy
for self-regulation of learning”; Klassen, Krawchuk, &
Rajani, 2008b). Thus, cognitive and behavioral strategies
to improve higher order executive processes should be
delivered in conjunction with attempts to build students’
confidence in their ability to achieve academic success.
CONCLUSION
College students are faced with multiple tasks and dead-
lines that need to be accomplished within designated
time frames, while much of their time is unstructured
and unregulated. Because delay behaviors can have seri-
ous negative consequences, much research has focused on
identifying the factors that produce and sustain academic
procrastination so that effective interventions may be
implemented. To our knowledge, this study was the first
to investigate subcomponents of self-reported executive
functioning associated with procrastination in a demo-
graphically diverse sample of college students. We found
that the domains of initiation, plan/organize, organiza-
tion of materials, inhibition, working memory, and task
monitoring significantly predicted academic procrasti-
nation in addition to increased age and lower consci-
entiousness. Overall, the conceptualization of academic
procrastination as a problem of executive dysfunction
holds promise for researchers, educators, and practition-
ers who seek to understand this behavior and apply
focused, strategic interventions to help alleviate its neg-
ative consequences.
Original manuscript received 8 April 2010
Revised manuscript accepted 17 August 2010
First published online 25 November 2010
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... The central premise of these studies was that mental effort entails subjective costs that could be dissociated from objective task costs, as quantified by indirect measures such as "time on task" and actual error rates. Even when people want to behave rationally and adhere to normative considerations, they may fail to make efficient use of internal and external cues to determine when to attend to a task (Kool & Botvinick, 2013Rabin et al., 2011). Recently, suggested that individuals' estimations of task effort are based on metacognition (Dunn, Lutes & Risko, 2016;, and as such, are largely inferential and are sensitive to preconceived biases, beliefs, and intuitive theories (Koriat, 2006). ...
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When people are confronted with multiple tasks, how do they decide which task to do first? Normatively, priority should be given to the most efficient task (i.e., the task with the best cost/benefit ratio). However, we hypothesize that people consistently choose to address smaller (involving less work) tasks first, and continue to focus on smaller tasks, even when this strategy emerges as less efficient, a phenomenon we term the “smaller tasks trap”. We also hypothesize that the preference for the smaller tasks is negatively related to individual differences in the tendency for rational thinking. To test these hypotheses, we developed a novel paradigm consisting of an incentive-compatible task management game, in which participants are saddled with multiple tasks and have to decide how to handle them. The results lend weight to the smaller tasks trap and indicate that individual differences in rational thinking predict susceptibility to this trap. That is, participants low in rational thinking preferred to start with a smaller (vs. larger) task and focused more on the smaller tasks regardless of their efficiency. Consequently, their overall performance in the task management game was significantly lower. We discuss the theoretical and practical implications of these findings and suggest possible interventions that may help people improve their task management.
... The literature shows that most studies have investigated the amount of procrastination among students [17,18], especially in the medical sciences. The results exponent the relationship between the tendency of procrastination with psychosocial factors, demographic, and other predictive variables [11,[19][20][21]. ...
... In some studies, procrastination has not been associated with age [21,22]. The study reported that variables such as age and performance range, including organizing and working memory, significantly predict procrastination, and among these factors, age is the most critical factor [17]. This finding is consistent with the study which examined the relationship between procrastination with demographic characteristics and distress and satisfaction. ...
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Aim This study aimed to investigate procrastination in nursing care providing. Design This descriptive cross-sectional study was carried out on 125 nurses in ICUs, PICUs, NICUs, and surgery wards, who were selected by census sampling in Iran. Methods The data were collected using the Procrastination Scale, which consisted of 25 items relating to 3 factors. Data were analyzed using statistics, Chi-square, Friedman test, analysis of variance, and Kolmogorov–Smirnov tests. Results Overall, 37% of the participants showed very high or high procrastination. Most of the procrastination was observed in the “Task aversion” (44.2%). ANOVA indicated that the mean total procrastination score had a significant relationship with age (p = 0.013), work experience (p = 0.006), and marital status (p = 0.02). Nurses with permanent employment (p = 0.014) and lower education (p = 0.009) and women (p = 0.023) were much more likely to procrastinate the provision of care. Conclusion It is recommended to adopt appropriate management strategies and take adequate measures to reduce procrastination, considering the existence of procrastination among nurses and its adverse impact on the quality of care.
... According to Noran (2000), a person who engages in procrastination is one who is confident on his or her strength to pursue all tasks effectively, plans to do so, and then fails, or delays it excessively before transferring their attention to other interesting pastimes. Although not all students are good at it, hence oftentimes, many of them experience struggle and worse failure in many learning areas (Rabin et al., 2011). ...
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COVID-19 affected all sectors, including academia, which resulted in an increase in online learning. While education continued through online platforms, various students-related problems arose, including improper time management, procrastination, and fluctuating academic performance. It is in this context that this quantitative study was carried out to determine how time management and procrastination affected students' performance in science and mathematics during the pandemic. We surveyed 650 Filipino high school students using the Procrastination Assessment Scale-Students and Wayne State University's Time Management questionnaire with a 0.93 reliability coefficient. The findings revealed that in science and mathematics, female students outperformed males. Eleven 12-year-olds had the highest mean grades in science and mathematics, while 15 16-year-olds had the lowest. Younger respondents (11-14) were more likely to have better time management in than older ones. Further, older respondents (15-18) procrastinate more than younger ones. Time management correlates positively with success in science and mathematics. Achievement in science and mathematics is the highest among students with good time management. Procrastination negatively affects achievement. High school students who procrastinated less fare better in mathematics. With this, the study opens possibilities for teaching older learners in time management to boost their performance. Students across ages should be urged to avoid procrastinating as it negatively affects academic performance. As reinforcement, schools may educate learners on time management and procrastination avoidance through orientations and other platforms.
... It was triggered by her anxiety of receiving unpleasant responses from her thesis supervisors since she had missed several consultation sessions. Anxiety commonly caused procrastination or intentional delay and avoidance of due task which negatively impacted learning, achievement, academic self-efficacy, and quality of life (Rabin et al., 2011). A previous study also reported that this behavior increased dropout rates and decreased graduation rates (Arnold, 2022). ...
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This present study explores how thesis writing anxiety causes delay in thesis writing completion along with its coping strategies. It adopted narrative inquiry as the research method. The participant was one former student of English education graduate program at a state university in Central Java, Indonesia. Data were garnered through semi-structured interviews. The data were then analyzed by employing thematic analysis. The results suggest that thesis writing anxiety was caused by family and financial problems, full-time working duties and poor time management, as well as unpleasant administrative staff services. Thesis writing anxiety empirically affected thesis writing completion in terms of thesis writing procrastination as well as lack of confidence and feeling worried of making mistakes. Successful coping strategies include identifying problems and seeking solutions, establishing mutual communication with thesis supervisors and workplace stakeholders, as well as maintaining self-motivation. Practical implications and suggestions for further studies are also discussed. DOI: 10.26905/enjourme.v7i2.8004
... La procrastinación académica ha sido definida como la tendencia a retrasar la realización de las tareas académicas previstas a pesar de las consecuencias negativas que pueda acarrear esta conducta (Gustavson & Miyake, 2017). La procrastinación académica se ha asociado con un menor rendimiento académico (Kim & Seo, 2015), así como con una baja satisfacción vital académica (Balkis & Duru, 2017 universitarios posponen sus tareas académicas en algún momento (Sommer & Haug, 2012), y entre un 30% y un 60% de ellos reconocen hacerlo de manera regular (Rabin, Fogel & Nutter-Upham, 2011). Factores personales de los estudiantes como baja autorregulación (Uzun, O'Callaghan, Bokszczanin, Ederer, & Essau, 2014), baja autoestima (Uzun, LeBlanc, & Ferrari, 2020), ansiedad (Custer, 2018) o depresión (Kınık & Revista Argentina de Ciencias del Comportamiento ISSN 1852-4206 Diciembre 2022 14, N°3, 32-40 revistas.unc.edu.ar/inde ...
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Este estudio investigó las relaciones entre la Tríada Oscura (maquiavelismo, narcisismo subclínico y psicopatía subclínica) y la procrastinación y deshonestidad académicas. Doscientos diez estudiantes universitarios españoles, con edades comprendidas entre los 18 y los 58 años, completaron las escalas Dirty Dozen, Academic Procrastination Scale-Short Form y una escala ad-hoc para evaluar la frecuencia de comportamientos académicos deshonestos. Se realizaron análisis de regresión jerárquica, controlando la edad y el sexo, se encontró que el maquiavelismo y el bajo narcisismo son predictores significativos de la procrastinación académica, mientras que el maquiavelismo es el único predictor significativo de la deshonestidad académica. El presente estudio contribuye a un mejor conocimiento de las causas de la procrastinación y la deshonestidad académicas.
... Our participants indicted deficits in SREM that might be connected with impairments in motivation regulation as deficit in WM highly impacts this ability resulting in some difficulties, including more challenging time motivating themselves to start and finish tasks that are not inherently interesting; overcoming difficulty to self-initiate; handling procrastination (Pallanti & Salerno, 2020); poorer academic achievement (compared to those without ADHD) and the need for external pressure to proceed essential life duties (Gareau et al., 2019;Prins et al., 2011;Rabin et al., 2011;Tuckman, 2012). ...
Article
One of the most comprehensive approaches to explaining attention-deficit/hyperactivity disorder (ADHD) symptoms is Barkley’s behavioral inhibition model (BBIM) (1997), in which behavioral inhibition (BI) plays a primary role. Due to the substantial role of working memory (WM) in explaining ADHD symptoms, Barkley recently updated his model and elevated WM from a mediator variable (in BBIM) to a primary position as an exogenous variable alongside BI, and titled his new model as Barkley’s updated executive functioning model (BUEFM). However, since the information about the explanatory power of the new model is sparse, this study aims to investigate the impact of this change in WM role by comparing these two models to explain ADHD symptoms. The study involved a sample of 184 (96 females and 88 males) undergraduate students with high ADHD symptoms who were selected using the purposive sampling method. For assessing models, we have utilized four tools that include: CNS-Vital Sign Test Battery; Barkley Deficit in Executive Functioning Scale; self-verbalization questionnaire (SVQ); and trail making test. We analyzed the data by running structural equation modeling (SEM) analysis using IBM AMOS software version 22. The results show that Model Comparison Measurement (e.g. AIC was 197.583 and 144.614 for BBIM and BUEFM, respectively) and Model Fit Indices (e.g. root mean square error of approximation (RMSEA) obtained 0.076 and 0.067 for BBIM and BUEFM, respectively) representing that BUEFM had a better value than BBIM, which means that the BUEFM was considered better fitting to the data. The findings of this study show that BUEFM has more Predictive power than BBIM to predict symptoms of ADHD through the motor control fluency (MOT) variable.
... Our participants indicted deficits in SREM that might be connected with impairments in motivation regulation as deficit in WM highly impacts this ability resulting in some difficulties, including more challenging time motivating themselves to start and finish tasks that are not inherently interesting; overcoming difficulty to self-initiate; handling procrastination (Pallanti & Salerno, 2020); poorer academic achievement (compared to those without ADHD) and the need for external pressure to proceed essential life duties (Gareau et al., 2019;Prins et al., 2011;Rabin et al., 2011;Tuckman, 2012). ...
Article
Abstract One of the most comprehensive approaches to explaining attention-deficit/hyperactivity disorder (ADHD) symptoms is Barkley’s behavioral inhibition model (BBIM) (1997), in which behavioral inhibition (BI) plays a primary role. Due to the substantial role of working memory (WM) in explaining ADHD symptoms, Barkley recently updated his model and elevated WM from a mediator variable (in BBIM) to a primary position as an exogenous variable alongside BI, and titled his new model as Barkley’s updated executive functioning model (BUEFM). However, since the information about the explanatory power of the new model is sparse, this study aims to investigate the impact of this change in WM role by comparing these two models to explain ADHD symptoms. The study involved a sample of 184 (96 females and 88 males) undergraduate students with high ADHD symptoms who were selected using the purposive sampling method. For assessing models, we have utilized four tools that include: CNS-Vital Sign Test Battery; Barkley Deficit in Executive Functioning Scale; self-verbalization questionnaire (SVQ); and trail making test. We analyzed the data by running structural equation modeling (SEM) analysis using IBM AMOS software version 22. The results show that Model Comparison Measurement (e.g. AIC was 197.583 and 144.614 for BBIM and BUEFM, respectively) and Model Fit Indices (e.g. root mean square error of approximation (RMSEA) obtained 0.076 and 0.067 for BBIM and BUEFM, respectively) representing that BUEFM had a better value than BBIM, which means that the BUEFM was considered better fitting to the data. The findings of this study show that BUEFM has more Predictive power than BBIM to predict symptoms of ADHD through the motor control fluency variable.
... Studies show that procrastination is quite usual for university students (Durak 2020;Kim and Seo 2015). Various studies in the related literature show that AP is a common behavior among university students (Day et al. 2000;Jin et al. 2019;Kathleen and Basaria 2021;Onwuegbuzie 2004;Rabin et al. 2011;Yockey 2016). Procrastination, which usually manifests itself in academic assignments such as writing papers, practicing for exams, and fulfilling reading homework every week, can negatively affect not only the student himself but also other people who trust him, even the organization (Zacks and Hen 2018). ...
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Psychological factors have a significant role in better understanding mechanisms that affect students' academic performance. The intense and long-term stress of the pandemic process has made it necessary to rethink the components which effect the academic achievement of pupils. The purpose of this study is to examine the variables that predict the academic achievement of university students during the pandemic process and to present a model on these variables. The study group has 241 students who continue their undergraduate education in Turkey. The data were collected with a self-description form and 6 scales. The partial Least Squares (PLS) Structural Equation Model was used to analyses the developed research model. In consequence of the study, a relationship was obtained between academic procrastination (AP) and multi-screen addiction (MSA). Covid-19 burnout has a crucial effect on AP, multiscreen addiction, and psychological well-being variables. Motivation and self regulation-attention variables are explanatory of AP. This study contributes to expanding the nomological network regarding the effects of Covid-19 on the psychological well-being and behavior of individuals.
Article
Purpose The aim of this paper is to offer insight into procrastination over the past decade using bibliometric analysis to gauge the evolving journey of this concept. Thus, the concept of procrastination is examined in terms of authors, affiliating institutions, countries, citation patterns, bibliometric coupling and co-occurrence analysis. Design/methodology/approach For exploring the research work on procrastination, the bibliometric analysis was conducted for co-authorship, co-occurrence of keywords, citation network analysis, most influential authors, document and country wise bibliometric coupling by taking 630 publications between the years 2010–2020 into consideration. Software like VOSviewer and Tableau was used for result analysis. In addition, the content analysis was used for the top research papers amongst the eleven different clusters. Findings The study reveals the nature and direction of research over the past decade on procrastination. The most prominent journals, authors, articles, institutions, countries and keywords have been identified. The topic shows an upward trend of research as no consolidation or maturity in the pattern is observed. Frontiers In Psychology had the highest number of publications followed by Personality And Individual Differences. The top three contributors are Sirosis, F.M., Feng, T. and Ferrari, J.R. The country-wise analysis shows the USA leading followed by Germany, China and Canada. UiT The Arctic University of Norway was having the most significant contribution followed by The Ohio State University, DePaul University and Tel Hai Academic College. The most prominent themes and documents are reported. In addition, the content analysis depicted the need to conduct the research work on the certain themes which may usher the researchers towards more conceptual clarity and strategizing. Originality/value Sufficient discourse and relevant literature are available about procrastination, bedtime procrastination and academic procrastination and related areas. However, procrastination is becoming a universal issue, especially in the field of human resources and workforce development. This paper attempts to facilitate the policy-makers, regulators, researchers and practitioners to explore allied and less explored areas of procrastination that need future investigation.
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In Sample 1, 46 procrastinators compared with 52 nonprocrastinators claimed lower self-esteem, greater public self-consciousness and social anxiety, and a stronger tendency toward self-handicapping. In Sample 2, 48 procrastinators compared with 54 nonprocrastinators reported a weaker tendency toward seeking self-identity information but a stronger tendency toward a diffuse-identity style, yet there were no significant differences in verbal and abstract thinking abilities. Further research must provide evidence for persistent procrastination as a personality disorder that includes anxiety, avoidance, and a fear of evaluation of ability.
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Academic procrastination was examined within a nontraditional population of commuting students (N = 386, M age = 25.64), who were ethnically, economically, and culturally diverse. Students completed a self-report measure, the PASS (Procrastination Assessment Scale-Students; Solomon & Rothblum, 1984). In comparison to previous results with traditional students, academic procrastination among nontraditional students was higher in reading weekly assignments and school activities in general, but lower in writing a term paper and attendance tasks. In comparison to previous results with African-American students, the present study found lower academic procrastination on attendance tasks and administrative tasks. Older students, women, and students born outside of the United States reported lower academic procrastination tendencies. No differences in reported academic procrastination were observed based on: ethnicity, whether students were the first members of their families to attend college, or whether students possessed a high school diploma. Finally, academic procrastination scores were negatively correlated with cumulative grade point average (GPA).
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Objective: Despite growing interest in adult attention deficit hyperactivity disorder (ADHD), little is known about its prevalence or correlates. Method: A screen for adult ADHD was included in a probability subsample (N=3,199) of 18-44-year-old respondents in the National Comorbidity Survey Replication, a nationally representative household survey that used a lay-administered diagnostic interview to assess a wide range of DSM-IV disorders. Blinded clinical follow-up interviews of adult ADHD were carried out with 154 respondents, oversampling those with positive screen results. Multiple imputation was used to estimate prevalence and correlates of clinician-assessed adult ADHD. Results: The estimated prevalence of current adult ADHD was 4.4%. Significant correlates included being male, previously married, unemployed, and non-Hispanic white. Adult ADHD was highly comorbid with many other DSM-IV disorders assessed in the survey and was associated with substantial role impairment. The majority of cases were untreated, although many individuals had obtained treatment for other comorbid mental and substance-related disorders. Conclusions: Efforts are needed to increase the detection and treatment of adult ADHD. Research is needed to determine whether effective treatment would reduce the onset, persistence, and severity of disorders that co-occur with adult ADHD.
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The potential bias in time estimation known as the planning fallacy was examined for 32 undergraduate students in relation to exam preparation. Students provided estimates of their study plans for the 8 days prior to two midterm exams. These estimates were compared to their study logs for the same period using Ordinal Pattern Analysis. The results indicate that overall students did not demonstrate optimistic biases as predicted by the planning fallacy. Moreover, a median split of the sample on measures of procrastination indicated that the students scoring high on procrastination were just as accurate in their study time predictions as low-scoring participants. Students scoring high on procrastination did commence studying later and studied significantly less than students in the low-procrastination group. No significant difference between these groups was found on exam performance. These results are discussed in relation to the nature of academic tasks, the significance of externally imposed deadlines and the effects of procrastination on task performance.
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Many important psychiatric disorders have a strong hereditary component, posing the problem of characterizing the biological mechanisms translating from the genetic level to that of complex social and behavioral abnormalities. The new field of imaging genetics uses neuroimaging methods to assess the impact of genetic variation on the human brain. Ideally, several imaging methods are used in conjunction to achieve an optimal characterization of structural-functional parameters in large groups of carefully screened individuals, whose genotype is then statistically related to these data across subjects. Imaging genetics is therefore a form of genetic association study. Although this approach is still relatively novel, the emerging literature shows that it can be used to identify neural processes involved in mediating the effect of genetic polymorphisms on psychiatric disease risk, contributing to the understanding of the pathophysiology of these complex disorders. We illustrate this approach using selected examples from genes involved in risk for schizophrenia (COMT, GRM3, DISC1, and G72), Alzheimer’s disease (APOE4), and depression, anxiety, and violence (5-HTTLPR and MAOA). Improved mechanistic understanding of psychiatric disease provides novel targets for future therapeutic interventions and may contribute to a more accurate biologically based nosology.
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This second edition of the bestselling textbook Personality Traits is an essential text for students doing courses in personality psychology and individual differences. The authors have updated the volume throughout, incorporating the latest research in the field, and added three new chapters on personality across the lifespan, health and applications of personality assessment. Personality research has been transformed by recent advances in our understanding of personality traits. This book reviews the origins of traits in biological and social processes, and their consequences for cognition, stress, and physical and mental health. Contrary to the traditional view of personality research as a collection of disconnected theories, Personality Traits provides an integrated account, linking theory-driven research with applications in clinical and occupational psychology. The new format of the book, including many additional features, makes it even more accessible and reader friendly.