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General and Specific Traits of

Personality and Their Relation to Sleep

and Academic Performance

Elizabeth K. Gray

David Watson

University of Iowa

ABSTRACT Few studies have examined the links between personality

variables and sleep and their combined effect on specific real-world

outcomes. Participants in this study completed numerous personality, sleep,

and performance measures; we examined the associations among these

measures. Personality was assessed using the Five-Factor Model. The

personality trait of Conscientiousness (especially its facet of Achievement

Striving) was a substantial predictor of academic performance. Analyses of

the sleep variables revealed three distinct constructs: quantity, quality, and

schedule. Sleep quantity showed few interesting correlates. In contrast, sleep

quality was associated with greater well-being and improved psychological

functioning, whereas sleep schedule (i.e., average rising and retiring times)

was significantly related to Conscientiousness, such that conscientious

individuals maintain earlier schedules.

The past 25 years have led to substantial changes in personalitypsychology. There is growing agreement on the nature of a trait

Journal of Personality 70:2, April 2002.

Copyright # 2002 by Blackwell Publishers, 350 Main Street, Malden, MA 02148,

USA, and 108 Cowley Road, Oxford, OX4 1JF, UK.

Elizabeth Gray and David Watson, Department of Psychology, University of Iowa,

Iowa City, IA.

We thank Jatin Vaidya for his help in data collection and for the development and

maintenance of the website.

Correspondence should be sent to Elizabeth Gray, Department of Psychology, E11

Seashore Hall, University of Iowa, Iowa City, IA 52242. Electronic mail may be sent

to [email protected].

structure of personality, and the accumulating data are establishingimportant relations between personality and various aspects offunctioning. This study will add to that literature by examining tworelated structures—the Big Five and the Big Three models ofpersonality—and their relations to two important areas of functioning:sleep and academic performance.

Models of Personality

‘‘Big-Five’’ (or Five-Factor) and ‘‘Big-Three’’ models of personalityare popular approaches in the current study of personality. The fivebroad traits of the Big Five—Neuroticism, Extraversion, Openness toExperience, Agreeableness, and Conscientiousness—have been re-liably and consistently replicated as a hierarchical structure ofpersonality (see John & Srivastava, 1999). A second research tradition,the Big-Three model of personality, articulates a structural model ofpersonality that includes three broad trait dimensions: Neuroticism,Extraversion, and Psychoticism or Disinhibition (see Tellegen, 1985;Watson & Clark, 1993).

Recent research indicates that the Big-Three and Big-Five modelsdefine rather similar trait structures. Most notably, it now is clearthat they share essentially equivalent dimensions of Neuroticism andExtraversion (e.g., Clark & Watson, 1999). Additionally, it isnoteworthy that Neuroticism and Extraversion are strongly andreliably correlated with measures of Negative and PositiveEmotionality, respectively (Watson & Clark, 1994); thus, thesetaxonomic schemes share a common ‘‘Big Two’’ of Neuroticism/Negative Emotionality and Extraversion/Positive Emotionality. Thethird Big-Three dimension—Psychoticism or Disinhibition—ishighly related to the Big-Five factors of (low) Agreeableness and(low) Conscientiousness. The final Big-Five factor, Openness toExperience, does not correlate substantially with any of the Big-Three dimensions.

The current study will include measures of both the Big-Five andBig-Three traits. Our specific focus will be on the broad domain ofConscientiousness versus Disinhibition due to its specific relation toperformance as well as its important implications for many aspects ofeveryday life, including emotional experience, the workplace, andpsychopathology. We will explore Conscientiousness more fully as ahierarchical trait by measuring its facets and their relations to sleep

178 Gray & Watson

and academic performance. Conscientiousness facets are expected toexhibit differential relations with sleep and academic performance.

In the hierarchical model of the Big Five, Conscientiousness can befurther subdivided into smaller traits or facets. Our study included thesix scales that define the NEO-PI-R domain of Conscientiousness.These facet scales are Competence, Order, Dutifulness, AchievementStriving, Self-Discipline, and Deliberation. Analyses of these lowerorder scales can further clarify the dispositional correlates ofConscientiousness. Costa, McCrae, and Dye (1991) define the facetsin the following manner: The Competence scale measures an overallpattern of being capable, sensible, and accomplished. Order indicatesthe tendency to keep one’s environment tidy and well-organized.Dutifulness is strict adherence to internalized standards of conduct.The Achievement Striving scale determines one’s need for achieve-ment and a striving towards excellence. The Self-Discipline facetindicates persistence, or an ability to continue with a task despiteboredom or distractions. And finally, Deliberation assesses individualdifferences in caution, planning, and thoughtfulness (Costa et al.,1991). These facets show adequate convergent and discriminantvalidity (Costa et al., 1991).

Personality and Performance

Given these characteristics of the Conscientiousness dimension, it isnot surprising that it is related to performance. Extensive research hasestablished that Conscientiousness makes a significant contribution tojob and academic performance. In particular, a meta-analysis byBarrick and Mount (1991) indicates that individuals high inConscientiousness generally perform better in work related situationsthan those who score low on this trait. Other recent research in theworkplace indicates that conscientious individuals tend to useproblem-focused coping (Shewchuk, Elliot, MacNair-Semands, &Harkins, 1999), set higher personal goals (Gellatly, 1996), and havehigher self-reported self-efficacy (Martocchio & Judge, 1997), whichmay mediate the relationship between Conscientiousness andperformance. Judge, Higgins, Thoresen, and Barrick (1999) alsoreported that Conscientiousness positively predicted both intrinsic (jobsatisfaction) and extrinsic (income and status) career success.Additionally, the absence of Conscientiousness may have importantimplications in the workplace; lack of Conscientiousness leads to poor

Personality, Sleep, and Academic Performance 179

productivity and lack of organization within a company (Hogan &Ones, 1997).

Studies also suggest that Conscientiousness influences academicperformance. Dollinger and Orf (1991) showed that Conscientiousnesswas the only trait to be a successful predictor of independent creditefforts, course grade, objective test performance, and punctuality ofcompleted assignments (Dollinger & Orf, 1991). Other studies showConscientiousness to be a general predictor of success in both gradeschool and graduate school (Digman, 1989). Musgrave-Marquardt,Bromley, and Dalley (1997) found Conscientiousness to be the bestpredictor of high grade point average (GPA). Similarly, Watson andClark (1993) reported that the related Big-Three dimension ofDisinhibition is an excellent predictor of academic performance.Students high in Disinhibition had significantly lower cumulativeGPAs than their peers. Moreover, Disinhibition was found to be asignificant predictor of college GPA even after controlling for SATscores and high school GPA (Watson & Clark, 1993).

Although empirical research consistently shows that Conscientious-ness is associated with superior performance, we know little aboutrelations at the facet level. What specific aspects of this broaddimension predict performance? One of the basic goals of this study isto examine the relation between Conscientiousness and performance atthe lower-order level using the facet scales of the NEO-PI-R. Given thecontent of the scales, we predicted that Achievement Striving would bemost highly related to performance. This personality characteristic ofachievement and motivation towards excellence should be reflected ina behavioral measure positively influenced by these same character-istics, namely grade point average.

Personality and Sleep

Our second variable of interest, sleep, has a complex relation topersonality. Unfortunately, the empirical evidence in this area issomewhat lacking, so our understanding of these relations isincomplete. Evidence for an association between sleep quantity andpersonality is particularly scarce. Hill, Diemer, and Heaton (1997)report nonsignificant correlations between estimated nightly sleepquantity and all of the Big Five dimensions (rs ranged from �.02[Neuroticism] to .12 [Conscientiousness]). Similarly, studies typicallyfind that amount of sleep is a weak predictor of mood and functioning

180 Gray & Watson

(e.g. Pilcher, Ginter, & Sadowsky, 1997; Verlander, Benedict, &Hanson, 1999). In contrast, a few studies have found significantrelations between quantity and situational factors. Verlander et al.(1999) found that sleep quantity was predicted consistently byenvironmental events; furthermore, Hawkins and Shaw (1992) foundthat the only significant association was that college students reportedspending more time in bed on the weekend than during the week.Because of this limited evidence, we did not make any specificpredictions for sleep quantity.

One well-established finding is that poor sleep quality issubstantially related to Neuroticism. Much of this work has beenconducted in clinical populations, but similar findings also have beenreported in analyses of college students and healthy adults (e.g.,Bonnet, 1985; Lacks & Morin, 1992; Pilcher & Huffcutt, 1996). Wetherefore predicted that Neuroticism would be related to poor sleepquality. Given the absence of data linking sleep quality to otherdimensions of personality, however, we made no predictions regardingthe other Big-Five or Big-Three traits.

Personality differences in sleep schedule are most often studied innormal populations through the morningness-eveningness paradigm(Horne & Ostberg, 1977; Smith, Reilly, & Midkiff, 1989). Individualdifferences in preferences for morning versus evening activity arerelated to personality, but the evidence is not entirely consistent. In theearly literature the most common pattern associated morning typeswith introversion and evening types with extraversion, consistent withthe folk stereotypes of late-evening party goers and quiet, responsiblemorning people (Jackson & Gerard, 1996; Larsen, 1985; Tankova,Adan, & Buela-Casal, 1994). It since has been shown, however, thatmorning people report higher positive affect throughout the day thanevening people (Clark, Watson, & Leeka, 1989) and that morningtypes exhibit higher optimism, satisfaction with life, Positive Emotion-ality, and Extraversion (Vaidya, 1997); these data indicate that theassociation between sleep schedule and personality is more complexthan the earlier data suggested.

In comparison to Extraversion, other traits have received lesssystematic attention in the morningness-eveningness literature. Areview by Tankova and colleagues (1994) reveals no significantassociations with Neuroticism. The data for Psychoticism, on theother hand, reveal an interesting pattern: High scores on the dimensionare consistently associated with greater eveningness; thus, evening


types tend to show an impulsive or disinhibited personality pattern(Mitchell & Redman, 1993). It is this relation with Psychoticism thatled us to predict that sleep schedule would be negatively associatedwith Conscientiousness.

This study will help to clarify this area by examining the associationbetween sleep schedule and personality more intensively. Based on themore recent evidence, we predicted that early rising would bepositively related to measures of Extraversion/Positive Emotionality.Additionally, indicators of Conscientiousness should be negativelycorrelated with sleep schedule; late risers will be low on Conscien-tiousness. Conversely, sleep schedule would be positively associatedwith measures of Disinhibition. Sleep schedule, however, should beunrelated to measures of Neuroticism/Negative Emotionality. Finally,in the absence of prior data, we made no predictions regarding eitherAgreeableness or Openness.

Sleep and Performance

Lastly, it is important to consider the relation between sleep andperformance. Although the focus of this study is personality,some interesting relations exist among sleep, performance, anddaily functioning.

Research generally indicates that sleep deprivation has overalldetrimental effects on an individual’s functioning and performance(Pilcher & Huffcutt, 1996; Tilley & Brown, 1992). In particular, sleepdeprivation impairs sustained attention and mood and has significanteffects on cognitive tasks (Gillberg & Akerstedt, 1998). Sleepdeprivation results in less vigilance, longer reaction times, lesscreativity, and more cognitive and perceptual distortions (see Horne,1988; Pilcher & Huffcutt, 1996; Porcu, Bellatreccia, Ferrara, &Casagrande, 1998; Tilley & Brown, 1992). These findings havesignificant implications for real-world performance measures in theworkplace and daily functioning, because chronic partial sleep depri-vation is a common condition in our society (Dement & Vaughn, 1999).

These general effects can also be studied as unique effects of threedimensions of sleep: quantity, quality, and schedule. Studies show thatpoor quality of sleep has detrimental effects on performance in theworkplace and in academic settings (e.g., Johnson & Spinweber,1983). Poor sleepers received significantly fewer promotions andpoorer grades, remained in lower pay grades, and had a significantly

182 Gray & Watson

higher attrition rate than good sleepers (Johnson & Spinweber, 1983).Sleep schedule also impacts performance in everyday settings.Performance on the job tends to be poorer on a rotating shift schedule(e.g., Jamal & Baba, 1992; Folkard & Monk, 1979; Tilley, Wilkenson,Warren, Watson, & Drud, 1982). Specifically, shift workers’ reactiontimes were significantly slower when working the night shift, reactiontime performance deteriorated over the course of the week, and jobsthat require attention, speed, accuracy, or vigilance consistently showthe poorest performance at night (Folkard & Monk, 1979; Gillberg &Akerstedt, 1998; Scott, 1994; Tilley et al., 1982). Disruption of shiftworkers’ circadian rhythm and sleep disturbance are thought to beprimary causes of decreased performance (Scott, 1994).

Sleep has also important implications beyond performance for dailyfunctioning. High quality sleep is essential to an individual’s healthand well-being. Thus, poor sleepers—that is, those who reportdifficulties falling asleep and/or awakening often during the night—often experience significant problems at work, at home, and in theirpersonal relationships (Lugaresi et al., 1983). They report significantlyhigher levels of depression and anxiety and exhibit greaterpathological tendencies (Johnson & Spinweber, 1983; Lugaresi etal., 1983; Monroe, 1967). In contrast, good sleepers–that is, those whoreport that their sleep is restful, enjoyable, and largely uninterrupted–tend to experience lower levels of distress and fewer problems(Monroe, 1967). In this regard, it is noteworthy that Pilcher, Ginter,and Sadowsky (1997) found that sleep quality is a much betterpredictor of health and well-being than is sleep quantity. Shiftwork canalso have detrimental effects on physical and psychological heath andsocial and domestic functioning as these employees find themselvesincreasingly out of synchronization with established social andphysiological rhythms (e.g., Barton, 1994; Frost & Jamal, 1979;Jamal & Baba, 1992; Luna, French, & Mitcha, 1997; Samkoff &Jacques, 1991; Scott, 1994).

Research indicates that sleep quality and sleep schedule areimportant predictors in performance and health (the implications ofsleep quantity are currently unclear). Unfortunately, many studies havenot measured all three dimensions of sleep in order to provide a clearpicture. The lack of studies in this area suggests a need for moreresearch in real-world settings. This study aims to further clarify theoverall relationship between sleep and performance. Based onprevious research, it is predicted that sleep quality and sleep schedule


will show the strongest relations with academic performance;specifically, superior performance will be related to better sleepquality and to a consistent sleep schedule that conforms closely to aday shift (i.e., an earlier, more morning-oriented schedule). Therelation between sleep and functioning will be examined, using thepersonality measures described earlier, especially those related toNeuroticism/Negative Emotionality.

CURRENT STUDY

The current study integrates all of the elements we have discussed. Asindicated earlier, one of its basic goals was to examine the associationsbetween personality and sleep more thoroughly. In addition, it isimportant to determine the joint influence of personality and sleep onbehavior, in that each clearly has important implications forperformance in many real-world situations. Few studies, however, haveexamined how these variables jointly influence behavior. Therefore, thecurrent research examined both personality and sleep in relation toacademic performance. We were particularly interested in exploring themerits of a model in which Conscientiousness is posited to play anetiological role in both high academic performance and better sleephabits. In other words, high Conscientiousness creates both (a) a strivingtoward superior performance in many areas of life and (b) responsibleand efficient sleep behavior. We expected that conscientious individ-uals would show earlier, more regular sleep schedules. Conscientiousindividuals were also predicted to exhibit superior academicperformance as reflected by higher grade point averages.

Furthermore, another basic goal of the study was to extend theexisting Conscientiousness literature by examining associations withsleep patterns and performance at the facet level. As discussedpreviously, we predicted that Achievement Striving would be aparticularly strong predictor of academic performance.

To accomplish these goals, study participants completed self-reportmeasures of the Big Three and Big Five; sleep quality, sleep quantity,and sleep schedule; and academic performance. Additionally, a subsetof the participants completed a daily sleep log via the Internet. Finally,academic performance data were collected from the Universityregistrar to provide an objective measure of behavior and to validatethe students’ self-rated GPA. These data were analyzed to clarify thelinks among personality, sleep, and academic performance.

184 Gray & Watson

METHOD

Population and Procedure

Participants were 334 undergraduate students (121 men and 213 women) at

the University of Iowa during the spring, summer, and fall sessions of 1998,

and the spring semester of 1999. Students received research participation

credit or extra credit for participation in the study. Most participants were

enrolled in an introductory psychology course. Ninety-four percent of

participants were between 18 and 21 years of age (M = 19.2, SD = 1.8). The

self-report data were collected in small group sessions that lasted approx-

imately 45 minutes.

Personality Measures

The personality measures included an adapted 96-item form of the NEO

Personality Inventory—a measure of the Big Five—created with special

permission from the publisher. This adapted instrument contained all of the

NEO-FFI items, plus the items that measure the six facets of Conscien-

tiousness from the full NEO-PI-R. The NEO-FFI is a short form of the

NEO-PI-R and contains 60 items, 12 for each of the Big-Five factors:

Extraversion, Neuroticism, Openness to Experience, Agreeableness, and

Conscientiousness. The revised measure contained 36 additional Conscien-

tiousness items (i.e., the complete Conscientiousness domain from the NEO-

PI-R) in order to measure Conscientiousness at the facet level. As noted

previously, these facets are Order, Dutifulness, Achievement Striving,

Competence, Self-Discipline, and Deliberation; each scale has eight items.

All items were rated on a 5-point Likert scale ranging from strongly agree to

strongly disagree.

Participants also completed the General Temperament Survey (GTS; Clark

& Watson, 1990). The GTS, designed to measure the Big-Three personality

traits, includes scales assessing Positive Temperament, Negative Tempera-

ment, and Disinhibition. The Disinhibition scale contains two subscales,

Carefree Orientation (13 items) and Antisocial Behavior (9 items; see Clark

& Watson, 1999): Carefree Orientation is inversely related to Conscientious-

ness, whereas Antisocial Behavior is negatively correlated with Agreeable-

ness. The GTS consists of 90 items answered in a true/false format.

Participants also completed the 60-item Expanded Form of the Positive

and Negative Affect Schedule (PANAS-X; Watson, Clark, & Tellegen,

1988). The PANAS-X was used to measure trait positive and negative affect,

which have been shown to be highly associated with Extraversion and

Neuroticism, respectively. Participants rated the extent to which they

generally felt a mood term using a 5-point scale, where 1 = very slightly

or not at all and 5 = extremely. In this study, only the general Positive and


Negative Affect scale scores were scored; they were computed for each

participant by summing the 10 terms for each scale.

Performance Criteria

To measure academic performance, participants estimated their current

college and final high school grade point averages. In addition, they were

asked to sign a waiver to permit us to verify these records with the registrar at

the University. Three hundred seven participants (90%) gave consent to obtain

their academic records from the university registrar. Of these 307 students, the

registrar was able to provide 294 students’ current University of Iowa GPA

and 200 students’ high school GPA. Average self-reported University of Iowa

GPA is based on 300 participants. Thirty-four subjects were unable to give an

appropriate estimate of their University of Iowa GPA due to the fact that they

participated in the study in their first semester at college before any tests or

grade reports were administered. Grade point averages for these students were

collected at the end of the term from the registrar.

Correlations between self-reported and actual University of Iowa GPA

(.84) and high school GPA (.92) were extremely high, giving us confidence in

the participants’ self-ratings. In addition, follow-up analyses indicated no

significant difference between the group who consented and the group who

did not give consent to verify academic records on either GPA or personality

variables; ts ranged from .00 to 1.09, all ps > .25. Consequently, self-rated

GPA will be used in subsequent analyses.

Sleep Measures

Participants completed the Pittsburgh Sleep Quality Index (PSQI; Buysse,

Reynolds, Monk, Berman, & Kupfer, 1989). The PSQI contains 18 items that

are rated by the participant. The PSQI usually contains six items to be rated by

the participant’s bed partner or roommate that were excluded in this study.

These questions are used for clinical information only and are not calculated in

the overall score. The PSQI assesses seven components of sleep including

subjective sleep quality, sleep latency, sleep duration, habitual sleep

efficiency, sleep disturbances, use of sleep medication, and daytime dys-

function. These seven components are summed to yield an overall global PSQI

score that has a range of 0 to 21. A higher PSQI score indicates poorer sleep

quality. The PSQI has been shown to have acceptable internal consistency,

reliability, and construct validity (Carpenter & Andrykowski, 1998).

The participants also completed a sleep questionnaire designed specifically

for this study; its items assessed perceived sleep quality and quantity as well

as attitudes about sleep and current symptoms of sleep deprivation (Sleep

186 Gray & Watson

Patterns and Attitudes Survey, SPAS; see Appendix). Thirty-eight items were

rated on a 5-point Likert scale, which ranged from strongly agree to strongly

disagree. On the basis of preliminary correlational analyses, seven items on

the SPAS were eliminated because they were highly redundant, correlating

.70 or higher with other SPAS items.

The 31 remaining items were factor analyzed using a principal factor

analysis with squared multiple correlations as communality estimates; two

factors emerged and were rotated using varimax. The first factor included 12

items that pertain to Subjective Sleep Inefficiency (see Appendix). A high

score on this factor indicates subjectively experienced sleep deprivation, such

that participants are reporting that they often feel tired. The second factor

included six items that pertain to Sleep Shallowness. A high scorer on this

factor endorses items such as ‘‘I am a very light sleeper’’ and ‘‘I wake often

during the night.’’

Sleep Logs

Participants also reported the number of hours they slept in an evening (to the

nearest half-hour) on average; this was used as one index of sleep quantity

(‘‘Average Sleep Duration’’). A second index of sleep quantity (‘‘Past Week

Duration’’) was computed as the average number of minutes slept per

evening from a 7-day sleep log.

Participants were asked to recreate their sleep schedule for the past week

by indicating their rising and retiring times each day for the previous 7-day

period. From this recreated sleep log, two sleep schedule variables were

calculated (in addition to the sleep quantity measure indicated above):

‘‘Average Rising Time,’’ the mean time the participant got up during this

week, and ‘‘Average Retiring Time,’’ the mean time the participant went to

bed. Sleep schedule as defined here is slightly different from the traditional

conception of morningness-eveningness in that it does not measure a

participant’s preference for morning/evening activities or energy levels

during these times. Several participants were unable to fully recreate their

sleep schedule to include the 7 prior days. Therefore, sleep data are based on

the results of 334 participants, except for Past Week Duration, Average

Rising Time, and Average Retiring Time. These sleep variables were

calculated only for participants with complete 7-day sleep logs (N = 271).

Note that sleep variables were adjusted to calculate sleep duration and to

correct for students’ late retiring times. Specifically, we converted rising and

retiring times to military time, adjusted morning times by adding 24 hours

(e.g., 0100 hours became 2500 hours), and converted all times to minutes.

These corrections allowed the researchers to calculate sleep duration as a

simple subtraction of retiring time from rising time, and to adjust the time

metric so that sleep schedule was a continuous variable. Thus, smaller values


indicate early rising and retiring, and large values indicate late rising and

retiring. Times were converted back to traditional clock values for illustration

in tables.

Participants were asked to continue their participation in the study by

keeping a daily log of their retiring and rising times in the 7 days following

their small group session. These data were collected via the Internet as a

reliability check on the retrospective data provided in the original session.

Participants logged on to the study web site and recorded their actual sleep

times each day. Ninety-eight participants completed this second part of the

study. These data demonstrated good test-retest reliability for the sleep

variables. The strong correlations between Average Rising Time (.59) and

Average Retiring Time (.73) in the recreated sleep logs and in the Internet

data indicate that sleep schedule is highly consistent from week to week. In

contrast, Web Week Duration and Past Week Duration were only

moderately (.43) correlated with one another. It is noteworthy, however,

that Average Sleep Duration correlated highly with both Web Week

Duration (.65) and Past Week Duration (.64), indicating that sleep quantity

is also consistent among weeks. This, therefore, gives us confidence in the

sleep log data provided by the participants in the original session. The

results reported subsequently will be based on the data collected in the

original re-created sleep logs.

RESULTS

Descriptive Statistics

Tables 1 and 2 present descriptive statistics for the sleep, personality,and academic performance variables. Mean personality scores arecomparable to the norms presented in the NEO-PI-R manual (Costa &McCrae, 1985) and GTS norms as reported by Watson and colleagues(e.g., Watson & Clark, 1993). Coefficient alphas for sleep andpersonality measures show reasonable internal consistency with theexception of two Conscientiousness facet scales with relatively lowcoefficient alphas, Competence (.61) and Dutifulness (.68).

Table 2 indicates that the average student in this sample arose at8:53 a.m. and retired at 1:21 a.m. The students estimated that they slept7.1 hours a night on the initial questionnaire, and reported that theyactually slept an average of 7.2 hours per evening on the 1-week sleeplog. However, the table also indicates that the students displayed hugeindividual differences in their characteristic sleep schedules. Forinstance, the average rising times in the sleep logs varied across anoverall range of more than 9 hours (from approximately 5 a.m. to well

188 Gray & Watson

after 2 p.m.); similarly, the average retiring times showed a range ofnearly 6 hours. Similarly, both measures of sleep duration varied acrossa range of more than 6 hours: Some students consistently reportedless than 5 hours of sleep per night, whereas others reported more than11 hours of sleep.

Table 2 also demonstrates that students were highly accurate inreporting their GPA. In fact, the mean self-reported high school GPAwas nearly identical to the corresponding value obtained from theregistrar. Similarly, the average self-rated University of Iowa GPA

Table 1Basic Descriptive Statistics for the Personality and Sleep Scales

Scale Mean SD Coefficient alpha

NEO scales

Conscientiousness (48) 160.68 22.54 .93

Extraversion (12) 42.16 6.99 .82

Neuroticism (12) 34.24 8.06 .84

Agreeableness (12) 43.53 6.01 .74

Openness to Experience (12) 40.02 6.83 .74

Conscientiousness facets

Order (8) 25.28 5.22 .77

Competence (8) 27.90 3.88 .61

Dutifulness (8) 29.24 4.62 .68

Achievement Striving (8) 26.60 4.74 .77

Self-Discipline (8) 23.45 4.73 .80

Deliberation (8) 24.27 5.13 .79

GTS Scales

Disinhibition (35) 13.63 6.88 .86

Positive Temperament (27) 18.60 6.08 .88

Negative Temperament (28) 12.51 7.29 .86

Disinhibition facets

Carefree Orientation (12) 5.40 3.44 .81

Antisocial Behavior (9) 3.35 2.59 .76

PANAS Positive Affect (10) 32.83 6.52 .87

PANAS Negative Affect (10) 20.52 6.85 .87

Sleep scales

PSQI (14) 5.72 2.67 .72

Subjective Sleep Inefficiency (12) 36.25 8.66 .85

Sleep Shallowness (6) 13.85 4.52 .83

Note. Number of items in each scale is presented in parentheses after scale title.


was only slightly higher (a difference of only .17 points) than thatobtained from actual University records. Thus, students can beexpected to report their academic performance quite accurately, atleast under circumstances in which they expect that their ratings willbe independently verified.

Correlations Among the Personality Scales

The correlations among the personality variables replicated previousfindings concerning the associations between the Big-Five and Big-Three dimensions, as well as specific relations to trait affect (Watson& Clark, 1994). Extraversion, Positive Temperament, and PositiveAffect showed moderate to high correlations with each other, withcorrelations ranging from .54 to .69. These data also replicated thestrong relations between Neuroticism, Negative Temperament, andNegative Affect, with correlations ranging from .63 to .74. Moreover,as expected, Conscientiousness and Agreeableness both weremoderately to strongly negatively correlated with Disinhibition(�.66 & �.39, respectively). As previously found, Openness was

Table 2Basic Statistics for the Average Sleep and Academic

Performance Variables

Mean SD Range

Sleep Variables

Average Sleep Duration 7.1 hours 1.2 hours 4.5 to 12.0 hours

Past Week Duration

(per night) 7.2 hours 1.3 hours 4.57 to 11.2 hours

Average Rising Time 8:53 a.m. 75 minutes 5:04 a.m. to 2:17 p.m.

Average Retiring Time 1:21 a.m. 74 minutes 10:17 p.m. to 4:13 a.m.

Grade Point Average

Estimated University of

Iowa GPA 2.89 .58 0.78 to 4.00

Actual University of

Iowa GPA 2.72 .71 0.50 to 4.00

Estimated High

School GPA 3.49 .44 2.20 to 4.00

Actual High

School GPA 3.48 .42 2.17 to 4.00

190 Gray & Watson

not substantially related to any of the other personality variables. Apartfrom the specific relations we have noted, the variables tended to showlow-to-moderate correlations with each other.

Correlations Between Personality andAcademic Performance

Associations between academic performance and the personalityvariables are shown in Table 3. It can be seen that within the Big Five,Conscientiousness is the best predictor of both college and high schoolGPA (.36 & .22, respectively). Not surprisingly, GPA is also predictedby Disinhibition, with correlations of �.42 (college GPA) and �.24

Table 3Correlations Between Personality Variables and GPA

Personality scale UI GPA HS GPA

Neuroticism/Negative Emotionality

Neuroticism .00 .00

Negative Temperament .03 .02

Negative Affect .03 .06

Extraversion/Positive Emotionality

Extraversion �.09 �.05

Positive Temperament .04 .01

Positive Affect .08 .07

Conscientiousness/Disinhibition

Conscientiousness .36* .22*

Order .15* .08

Competence .23* .13

Dutifulness .28* .27*

Achievement Striving .39* .22*

Self-Discipline .36* .19*

Deliberation .27* .15*

Disinhibition �.42* �.24*

Carefree Orientation �.41* �.27*

Antisocial Behavior �.30* �.10

Other Personality Traits

Openness .18* .01

Agreeableness .15* .11

Note. N = 300; * p < .01; Correlations above |.30| are presented in bold.


(high school GPA). These findings are consistent with previousresearch establishing that measures of Conscientiousness versusDisinhibition are substantial predictors of academic performance(e.g., Digman, 1989; Dollinger & Orf, 1991; Musgrave-Marquardtet al., 1997; Watson & Clark, 1993), and confirm a basic hypothesis ofthe study. Neither college GPA (rs ranged from .00 to .18) nor highschool GPA (rs ranged from .00 to .11) were strongly or consistentlypredicted by any of the other Big-Five or Big-Three dimensions.

Perhaps the most interesting relationships with GPA are those of theConscientiousness facets. As expected, we found striking specificitybetween the six facets of Conscientiousness and college GPA, withcorrelations ranging from only .15 (Order) to .39 (AchievementStriving). Moreover, follow-up tests indicate that college GPA hadsignificantly greater correlations with both Achievement Striving (.39)and Self-Discipline (.36) than with Order (.15), Competence (.23), andDutifulness (.28). High school GPA showed a very similar pattern.Order (.08) and Competence (.13) again showed the lowestcorrelations with academic performance, whereas the strongestrelations were seen with Dutifulness (.27), Achievement Striving(.22), and Self-Discipline (.19). Not surprisingly, disciplined individ-uals with a high motivation to succeed tend to do well in school,whereas individual differences in neatness essentially are unrelated toacademic performance.

To clarify these lower-order relations further, we conducted forwardmultiple regression analyses using the Conscientiousness and Disin-hibition facet scales as potential predictors and the two academicperformance scores as criteria. The results are shown in Table 4.Carefree Orientation was the strongest predictor of college GPA,accounting for 16% of the criterion variance; Achievement Striving(3% of the variance) also made a significant incremental contributionafter Carefree Orientation. Multiple regressions using the Conscien-tiousness facets only revealed Achievement Striving as the strongestpredictor of college GPA as hypothesized (15% of the variance). Incontrast, only Dutifulness contributed significantly to the prediction ofhigh school GPA. (See Table 4.)

Correlations Among the Sleep Variables

The interrelations among the sleep variables indicated that these sevensleep variables define three basic constructs: sleep quantity, sleep

192 Gray & Watson

quality, and sleep schedule. Sleep Shallowness was not a goodindicator of any of the three sleep constructs; it showed no significantcorrelations with the other sleep variables. Two measures of sleepquantity, Average Sleep Duration and Past Week Duration, correlated.64 with one another. The PSQI and Subjective Sleep Inefficiencydefined the sleep quality construct, correlating .46 with each other.Finally, Average Rising Time and Average Retiring Time—indicatorsof sleep schedule — correlated .65 with each other.

Correlations Between Personality and Sleep

We now consider the relations between personality and the sleepvariables. We begin by noting that neither Agreeableness norOpenness showed any significant relations with any of the sleepmeasures. In terms of the sleep variables, our two measures of sleepquantity—Average Sleep Duration and Past Week Duration—showed few associations with personality. In fact, the only

Table 4Summary of Regression Analyses for Personality Variables Predicting

Grade Point Average

Variable R2 R2 Change B SE B b

Analyses of NEO-PI-R and GTS facets

UI GPA

Step 1: Carefree Orientation .16* .16* �4.37 1.21 �.26*

Step 2: Achievement Striving .20* .03* 3.07 .80 .25*

Step 3: Antisocial Behavior .21 .02 �3.12 1.29 �.14

Step 4: Order .23 .01 �1.35 .67 �.12

High School GPA

Step 1: Dutifulness .07* .07* 1.87 .62 .21*

Step 2: Carefree Orientation .09 .02 �2.62 .86 �.22*

Step 3: Order .10 .01 �1.04 .51 �.13

Analyses of NEO-PI-R facets only

UI GPA

Step 1: Achievement Striving .15* .15* 3.24 .96 .27*

Step 2: Self-Discipline .16 .01 �1.99 .96 .16

High School GPA

Step 1: Dutifulness .07* .07* 2.41 .50 .27*

Note. N = 300; * p < .01.


significant association was that Deliberation correlated .20 with PastWeek Duration; in other words, individuals who are cautious andthoughtful reported more sleep over the past week. It is noteworthy,however, that this scale was not significantly correlated withAverage Sleep Duration (.14). Therefore, it seems reasonable toconclude that sleep quantity essentially is unrelated to the basic traitsof personality.

Similarly, Sleep Shallowness was only weakly related topersonality. Its only significant association was a significant positivecorrelation with Deliberation (.15). Thus, our data revealed a slighttendency for cautious and thoughtful individuals to report sleepingmore lightly.

Table 5 presents the correlations between the personality scales andmeasures of sleep quality and sleep schedule. As predicted, our twomeasures of sleep quality (the PSQI and Subjective Sleep Inefficiency)were moderately associated with markers of Neuroticism/NegativeEmotionality, with correlations ranging from .38 to .48. Thus,replicating previous research in this area, poor quality sleep is broadlyassociated with greater distress and poorer psychological functioning.

In addition, our data extend the current literature by linking poorsleep quality to both low Extraversion/Positive Emotionality and lowConscientiousness. Specifically, our indicators of sleep quality hadcorrelations ranging from �.13 to �.43 with markers of Extraversion/Positive Emotionality, and from �.10 to �.42 with Conscientiousnessand its facets. Follow-up tests indicated that the Conscientiousnesslower order correlations of Self-Discipline and Competence differedsignificantly from Order for both Subjective Sleep Inefficiency andthe PSQI (see subscripts on Table 5). These latter results areparticularly interesting in that the Conscientiousness domain has notbeen extensively investigated in sleep research. Our data, however,indicate that conscientious individuals consistently report better,higher quality sleep.

As discussed earlier, sleep quality variables have been reliably relatedto individual differences in Neuroticism/Negative Emotionality. Weconducted hierarchical multiple regression analyses to determinewhether any other personality scales made significant, independentcontributions to the prediction of sleep quality. We began by forcing inmarkers of Neuroticism/Negative Emotionality as a block in Step 1. Wethen conducted forward regression analyses, using the scales thatcorrelated significantly with sleep quality (see Table 5) as possible

194 Gray & Watson

predictors. The results are presented in Table 6. After controlling forNeuroticism/Negative Emotionality, Positive Affect significantlypredicted sleep quality (accounting for 2% and 7%, respectively, ofthe variance in the PSQI and Subjective Sleep Inefficiency). Thus, traitPositive Affect makes a significant incremental contribution to theprediction of sleep quality. Alternately, Conscientiousness andExtraversion made modest contributions only to the prediction of

Table 5Correlations Between Personality, Sleep Quality, and Sleep

Schedule Variables

Personality Scale

SubjectiveSleep

Inefficiency PSQI

AverageRisingTime

AverageRetiring

Time

Neuroticism/Negative Emotionality

Neuroticism .45* .39* �.01 .03

Negative Temperament .43* .39* .00 .04

Negative Affect .48* .38* .09 .12

Extraversion/Positive

Emotionality

Extraversion �.17* �.13 .06 .13

Positive Temperament �.34* �.21* �.18* �.08

Positive Affect �.43* �.29* �.12 �.12

Conscientiousness/Disinhibition

Conscientiousness �.41* �.26* �.31* �.38*Order �.25*bcd �.16*bdf �.17*d �.23*a

Competence �.39*a �.29*a �.22*bc �.29*a

Duty �.30*b �.15bde �.31*ac �.31*a

Achievement

Striving �.34*b �.22*ace �.33*a �.33*a

Self-Discipline �.42*a �.25*ac �.28*ace �.29*a

Deliberation �.27*bc �.18*bce �.15d �.32*a

Disinhibition .19* .18* .39* .46*Carefree Orientation .18* .10 .39* .41*Antisocial Behavior .12 .20* .25* .33*

Other Personality Traits

Openness .12 .09 .07 .08

Agreeableness �.14 �.13 �.11 �.12

Note. N = 271; * p < .01; Correlations above |.30| are presented in bold. Correlations in

the same column that share the same subscript are not significantly different from one

another, (p < .05, two-tailed).


Subjective Sleep Inefficiency; neither scale contributed significantly inthe analysis of the PSQI. Consequently, these scales do not appear to besubstantial, independent predictors of sleep quality.

The third sleep construct, sleep schedule, also shows interestingassociations with personality. It is noteworthy that—with the exceptionof a small but significant correlation between Average Rising Time andPositive Temperament (.18)—these associations are specificallyconfined to the broad domain of Conscientiousness versus Disinhibi-tion. Table 5 indicates that both indicators of sleep schedule weremoderately related to the Conscientiousness domain score (�.31 and�.38) and to Disinhibition (.39 and.46). However, these associations arebetter understood at the lower-order level. Note, for example, thatAverage Rising Time and Average Retiring Time both were moderatelycorrelated with Achievement Striving (�.33 and �.33, respectively),but were only weakly related to Order (�.17 and�.23, respectively). Asnoted by subscripts in Table 5, follow-up tests indicated that thecorrelations for Order and Achievement Striving differed significantlyfor Average Rising Time but not Average Retiring Time.

Further regression analyses helped to clarify the lower-orderrelations between sleep schedule and the Conscientiousness versusDisinhibition domain. We first conducted forward regression analysesusing the NEO-PI-R and GTS facet scales that correlated significantly

Table 6Summary of Regression Analyses Predicting Sleep Quality While

Controlling for Neuroticism/Negative Emotionality


PSQI

Step 1: Negative Affect .06 .03 .17

Negative Temperament .05 .03 .15

Neuroticism .17* .17* .02 .03 .06

Step 2: Positive Affect .20* .02* �.07 .02 �.18*

Subjective Sleep Inefficiency

Step 1: Negative Affect .32 .09 .25*

Negative Temperament .19 .09 .16

Neuroticism .27* .27* .02 .09 .02

Step 2: Positive Affect .34* .07* �.45 .09 �.33*

Step 3: Conscientiousness .37* .03* �.07 .02 �.18*

Step 4: Extraversion .39* .02* .22 .07 .17*

Note. N = 334; * p < .01.

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with sleep schedule (see Table 5). The results are shown in Table 7.The GTS Carefree Orientation subscale was the strongest individualpredictor of sleep schedule, accounting for 16% and 16% of thevariance in Average Retiring Time and Average Rising Time,respectively. In addition, the GTS Antisocial Behavior subscale madea modest incremental contribution to the prediction of AverageRetiring Time (2% of the variance), whereas the NEO-PI-RDeliberation (2% of the variance) and Dutifulness (2% of thevariance) facets added significantly in the analyses of Average RisingTime. We next conducted a parallel set of analyses, using only theNEO-PI-R facet scales as predictors. These analyses indicated thatDeliberation (10% of the variance in Average Retiring Time) andAchievement Striving (3% and 10% of the variance in AverageRetiring and Average Rising Times, respectively) were individualpredictors of sleep schedule, accounting for significant percentages ofthe variance in both criteria. (See Table 7.)

These data clearly support the goals of this study and aspects of ourmodel of personality and sleep. As discussed previously, wehypothesized that the trait of Conscientiousness plays an etiological

Table 7Summary of Regression Analyses for Personality Variables Predicting

Sleep Schedule


Joint analyses of NEO-PI-R and

GTS facets

Average Retiring Time

Step 1: Carefree Orientation .16* .16* 50.50 9.53 .33*

Step 2: Antisocial Behavior .18* .02* 33.24 12.81 .16*

Average Rising Time

Step 1: Carefree Orientation .16* .16* 62.95 13.65 .41*

Step 2: Deliberation .15* .02* 22.18 8.04 .22*

Step 3: Dutifulness .17 .02 �17.55 7.91 �.16

Analyses of NEO-PI-R facets only

Average Retiring Time

Step 1: Deliberation .10* .10* �23.06 6.63 �.23*

Step 2: Achievement Striving .14* .03* �22.26 7.11 �.21*

Average Rising Time

Step 1: Achievement Striving .10* .10* �34.33 6.56 �.31*

Note. N = 271; * p < .01.


role in better sleep habits. Specifically, conscientious individuals werepredicted to have better sleep habits and earlier rising and retiringtimes. Table 5 supports this aspect of the model by establishingmoderate associations between Conscientiousness and sleep schedule.

Correlations Between Sleep andAcademic Performance

Thus far, we have seen that measures of Conscientiousness versusDisinhibition are significant predictors of both sleep (sleep quality andsleep schedule) and academic performance. Do the sleep variables alsopredict academic performance? Table 8 presents the correlationsbetween both GPA measures and various indicators of sleep. Thecorrelations are invariably low, and only two (those between collegeGPA and Average Rising Time/Average Retiring Time) reachsignificance. Thus, we see little evidence that academic performancecan be linked to individual differences in sleep quality, sleep quantity,or sleep schedule.

DISCUSSION

Summary of Findings

This study replicates and extends previous research on sleep,personality, and academic performance. Although earlier studies haveexamined how personality relates to sleep and academic performanceseparately, the current study is the first to examine all three types ofvariables in the same participants.

Table 8Correlations Between GPA and Sleep Variables

Sleep Variable UI GPA HS GPA

Average Sleep Duration �.05 �.10

Past Week Duration �.08 �.09

PSQI �.11 �.06

Subjective Sleep Inefficiency .00 .04

Sleep Shallowness .10 .01

Average Rising Time �.19* �.17*

Average Retiring Time �.13 �.10

Note. N = 271; * p < .01.

198 Gray & Watson

Personality and academic performance. Consistent with previousresearch in this area (e.g., Digman, 1989; Watson & Clark, 1993),our results indicated that measures of Conscientiousness versus Dis-inhibition were substantial predictors of academic performance. It isnoteworthy that all of the other assessed traits had weak andgenerally nonsignificant correlations with academic performance,indicating that the association is specific to this particular domain.Moreover, our data extend the existing evidence by establishingfurther specificity at the lower-order level. As predicted, the NEO-PI-R Achievement Striving facet scale was a particularly goodpredictor of academic performance, especially college GPA; incontrast, individual differences in Order essentially were unrelated toacademic performance. These results support a hierarchical model ofpersonality and demonstrate the utility of examining relations at thespecific, lower-order level.

Personality and sleep. The sleep constructs correlated quitedifferently with trait scores. First, sleep quantity—that is, theactual number of hours slept—was not found to be a substantialpredictor of any of the personality scales included in this study. Mostsurprisingly, sleep quantity was completely unrelated to multiplemeasures of both Negative and Positive Emotionality. In other words,the duration of sleep was entirely unrelated to long-term well-being.Although these findings are contrary to popular beliefs thatemphasize the importance of getting enough sleep, they areconsistent with the results of earlier studies indicating that sleepquantity is a relatively weak predictor of personality and affectivity(e.g., Hill, Diemer, & Heaton, 1997; Pilcher et al., 1997; Totterdell,Reynolds, Parkinson, & Briner, 1994).

It could be argued that these negative results might be at leastpartly due to our use of college students as participants. Comparedto working adults, college students have considerable flexibility intheir sleep schedules. For instance, if a night’s sleep is shortened bystudying or some other late-night event, college students often havean opportunity to remedy that sleep loss by sleeping later the nextmorning or by taking a nap in the afternoon. Adults who mustmaintain fixed, rigid work schedules may lack this luxury. Anotherrelated explanation is that the participants in this naturalistic studyhad considerable control over the amount of sleep they weregetting. It may be that most people eventually are able to


identify—perhaps after a lengthy process of trial and error—theamount of sleep that they need to maintain reasonable levels ofwell-being. In other words, it may be that the students who reportedgetting less sleep actually needed less sleep to maintain a generallyeuthymic mood. More research is needed to clarify how sleepquantity relates to long-term emotionality and other aspects ofpsychological functioning.

In contrast, sleep quality was consistently related to indicators oflong-term well-being. Replicating previous research in this area, ourresults indicate that individuals high in Neuroticism/Negative Emo-tionality reported lower overall sleep quality, with correlations rangingfrom .43 to .48 with Subjective Sleep Inefficiency and from .38 to .39with the PSQI. Furthermore, our results extend this earlier evidence byestablishing that sleep quality also is related to measures ofExtraversion/Positive Emotionality. Most notably, trait Positive Affectwas moderately correlated with both Subjective Sleep Inefficiency(�.43) and the PSQI (�.29); moreover, Positive Affect contributedsignificantly to the prediction of both measures of sleep quality, evenafter controlling for individual differences in Neuroticism/NegativeEmotionality (see Table 6).

Finally, our data help to clarify how personality is related toindividual differences in sleep schedule. As discussed earlier, previousstudies in this area have yielded complex and inconsistent results,particularly with regard to measures of Extraversion/Positive Emo-tionality. Our results indicate that sleep schedule, essentially, isunrelated to individual differences in Extraversion/Positive Emotion-ality, Neuroticism/Negative Emotionality, Agreeableness, and Open-ness. Instead, sleep schedule is rather specifically related to individualdifferences in Conscientiousness versus Disinhibition, with disinhi-bited (low Conscientiousness) types maintaining a later, moreevening-oriented schedule. Moreover, given the significant linksbetween this trait dimension and academic performance, it is notsurprising that sleep schedule was the only sleep construct to besignificantly related to college GPA (see Table 8). Overall, ourfindings suggest that early risers tend to be more disciplined,achievement-oriented individuals than evening types.

Basic analyses of sleep. The current study adds some unique findingsto the sleep literature. It is noteworthy that although our results arebased on a nonclinical sample, we identified many of the same trait

200 Gray & Watson

correlates of sleep as were found previously in clinical populations. Ofprimary interest, however, is the distinction among the specific sleepconstructs found in this study. This distinction has potentiallyimportant implications for measuring and assessing sleep in differentpopulations. In this study, we found that sleep variables appear toreflect three distinct constructs (sleep quality, sleep quantity, sleepschedule), which have very different implications for well-being andperformance. First, sleep quality, measured by the PSQI and SubjectiveSleep Inefficiency in this study, reflects a person’s perception of therestfulness of his or her sleep versus problems in his or her sleeppatterns. These sleep perceptions included subjective assessments ofhow the respondents felt during the day (tired, unable to concentrate),as well as difficulties related to falling and staying asleep. Sleepquantity refers to the overall amount of sleep a person is getting and isan objective measure of the actual length of sleep. Finally, sleepschedule—assessed in our study by the participants’ average rising andretiring times—reflects individual differences in the temporalpatterning of sleep. In order to ensure that sleep is assessed in areasonably comprehensive manner, we recommend that researchersinclude measures of all three constructs in future studies.

Additionally, our measure of sleep quality, Subjective SleepInefficiency, as measured by the Sleep Patterns and Attitudes Survey(SPAS), may be a useful scale for further research on sleep quality innormal samples. The SPAS is short and easy to administer, and both ofits scales have good internal reliability. Moreover, the scoring of thesescales is quite simple in comparison to the complex formulas used tocalculate scores on the PSQI.

Limitations and Future Directions

There are two key limitations that should be mentioned regarding thecurrent study. First, the sample assessed is comprised entirely ofcollege students, mostly in their 1st or 2nd year. As mentioned before,college students have a level of freedom in planning their class, study,and sleep schedules that most adults in the workforce do not possess.College students, for example, typically are not required to modify theirsleep preferences to conform to an 8 a.m. to 5 p.m. workday, as aremany adults. An older sample, therefore, may show quite differentpatterns with regard to sleep schedule and how it relates to thebasic traits of personality. Second, this study was based primarily on


self-report data. Although they offer many advantages, self-ratings maybe subject to distortions in retrospective recall and may also beinfluenced by social desirability and other response biases.

In light of these limitations, future research in this area shouldextend this study by examining the associations among these variablesin an older, adult population; it also would be helpful to includeadditional non-self-report measures of the key constructs. With regardto the latter, alternate measures—such as roommate or spouse ratingsof personality and sleep habits, or behavioral or biological measures ofsleep—may play an important role in clarifying the nature of therelations among personality traits, sleep habits, and performance.

With regard to the former, it would be interesting to attempt toreplicate and extend these findings by examining how personality andsleep relate to the functioning of adults in the workplace. Similarindicators of sleep and personality could be used in this context;instead of academic performance, job-related performance could beassessed, using supervisor evaluations of employee effectiveness and/or objective measures of productivity. In addition, measures of health,job satisfaction, and work-related affect could be used to assess howsleep and personality are linked to overall functioning in theworkplace. As noted earlier, because working adults may have lesscontrol over their sleep schedules and many other aspects of theirlifestyle, this type of study would provide an interesting test of thegeneralizability of the current findings.

In summary, the present findings establish the existence of threedistinct aspects of sleep in daily life. They also demonstrate importantlinks between general and specific personality traits, academicperformance, and sleep. Because few studies have measured all threetypes of variables simultaneously, these findings suggest the need forfurther research in this area.

Appendix

Sleep Patterns and Attitudes Survey

Sleep Inefficiency Scale

___ I feel rested most days. (RK)

___ I dread getting up in the morning.

___ I often need an alarm clock to wake up at the appropriate time.

___ On weekday mornings I hit ‘‘snooze’’ several times to get more sleep.

202 Gray & Watson

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___ It’s a struggle for me to get out of bed in the morning.

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