internet overuse and personality 1 running head: internet overuse

Internet Overuse and Personality 1

Running head: INTERNET OVERUSE AND PERSONALITY

Internet Overuse and Personality: A Look at the Big Five, BIS/BAS, Loneliness, &

Boredom

Jared Salisbury

Adviser: William Revelle

Second Reader: Benjamin Gorvine


Abstract

Psychologists have been concerned with Internet overuse for many years. Much of the

research relating this phenomenon to personality has emphasized social uses of the

Internet and has concluded that people with social deficits (low Extraversion, high

Neuroticism, high Loneliness) use the Internet for socializing and overuse it as a result.

The present research finds this line of thinking is outdated and limited in scope, and

posits that Internet overuse can be thought of in terms of information seeking and lack of

impulse control. A new subjective Internet overuse scale was developed and analyzed

along with reports of amount and type of Internet use, the Big Five dimensions of

personality, Behavioral Inhibition and Behavioral Approach scales from Gray’s

Reinforcement Sensitivity Theory of personality, and scales assessing Loneliness and

Boredom. A survey was administered online and analyzed using the Synthetic Aperture

Personality Assessment technique. Results show that low Conscientiousness is the best

predictor of Internet overuse, supporting the impulse control hypothesis, but do not rule

out social deficits. Emphasis is placed on the wide variety of motivations for and types of

Internet use, and the benefit of taking a fresh, unbiased look at human interaction with

the medium.


Internet Overuse and Personality: A Look at the Big Five,

BIS/BAS, Loneliness, & Boredom

Consider the following anecdote: Two college friends are up late one night, deep in

rambling conversation. Mention of an avocado sparks the inevitable question, bound to

arise once and only once in such a setting, when the mysterious green lump rears its

bumpy head: Is an avocado a fruit or a vegetable? Being that these two friends are living

in the United States in the early 21st Century, their course of action is obvious: consult

Wikipedia. They open a browser on the conveniently placed, already booted, wi-fi capable

laptop sitting before them and commence their search. Within minutes, the original

question seems trivial; of course an avocado is a fruit. The challenge becomes classifying it

as a true fruit (as opposed to a false fruit, which is composed of other parts of the plant in

addition to the ovum) and as a simple fruit (as opposed to an aggregate fruit, which is

composed of more than one ovum). In 15 minutes time, the friends have become veritable

fruit experts, capable of categorizing even the most enigmatic of ripened plant ovaries.

Never before in human history has information been so accessible, immediate, and

densely distributed. The words Internet and World Wide Web are lobbed about so

frequently we often overlook their connotations; the Internet is quite literally a vast web of

interconnected information. For an illustration of this, consider another phenomenon that

Wikipedia makes possible, which was also most likely borne of the minds of bored

American college students. I refer to the ”Wikipedia Game,” in which two or more players

race to navigate from one entry to another, completely unrelated one using only the

hyperlinks on each page. More surprising than the fact that this is actually entertaining is

that a skilled player usually only takes a few minutes to get from Michael Jackson to

Pharmacogenomics.

Wikipedia provides some of the most concrete examples, but all applications of the

Internet boil down to the consumption of information. As such, the Internet provides a


potent means for the expression of the ”seeking” mechanism identified in neuroscience

literature. Closely associated with the dopaminergic system, the seeking mechanism is

what drives us to seek out new information related to goals. Rather than experiencing

pleasure (the function of the opiate system), the dopaminergic system reinforces behaviors

which lead to pleasure. Without satiety built in, it can easily become problematic, and is

associated with substance abuse and addictive behaviors (Panksepp, 1998). As anyone

who has ever felt compelled to check e-mail incessantly, follow irrelevant links on websites,

or look up useless information can attest, information seeking can easily get out of hand,

and the Internet provides the perfect environment for such behavior.

Since its conception, the Internet has rapidly become a bigger and bigger part of

more and more people’s lives. One would be hard pressed to find an American college

student who does not use the Internet on a daily basis. The notion of a paperless

workplace is gradually becoming a reality for many. Smart phones, such as Blackberries

and iPhones, have lifted the constraints of time and place. As the Internet becomes more

ubiquitous, the need for a more complete understanding of the human animal’s interaction

with it becomes all the more pressing. With the vast majority of the industrialized world

getting persistent exposure to the Internet, the question is not who uses the Internet

often, but for whom is its use likely to be problematic? The present research attempts to

answer this question by applying theories and measures from personality psychology.

The application of psychology to the study of the Internet is by no means a new

field. Wisely, researchers have recognized the ever-growing importance of the new medium

since its early days. A large portion of the literature has been devoted to the discussion of

”Internet addiction” or ”pathological Internet use,” a compulsion to use the Internet

which interferes with everyday functioning. And with good reason: a recent metasynthesis

of Internet addiction studies estimated that nearly 9 million Americans may be labeled

Internet addicts (Byun et al., 2009). However, it remains problematic that no common


definition for Internet addiction has been decided upon; a good working definition comes

from Beard (2005): ”An individual is addicted when an individual’s psychological state,

which includes both mental and emotional states, as well as their scholastic, occupational

and social interactions, is impaired by the overuse of the medium.” The most common

scales used to diagnose Internet addiction are derived from those found in compulsive

gambling literature, and the common brain chemistry involved in each suggests a

theoretical link. A large study conducted in Taiwan reported comorbidity of Internet

addiction with substance abuse, lending further credence to the notion that Internet

addiction shares many of the characteristics of other addictions (Ko et al., 2006).

Despite some methodological inconsistencies and disagreement over a formal

definition, the evidence indicates that Internet addiction is a very real, relatively

widespread disorder (Byun et al., 2009). Significant attempts have been made to apply

personality psychology–to see who Internet addicts are–particularly within the framework

of the Big Five dimensions of personality. The Big Five are Openness to Experience,

Conscientiousness, Extraversion, Agreeableness, and Stability, five personality traits which

have consistently emerged as essential and universal facets of personality (Goldberg, 1990).

Studies on the Big Five and Internet addiction have generally concluded that Internet

addicts score high on Neuroticism (the opposite of Stability) and low on Extraversion

(Hamburger & Ben-Artzi, 2000). Of the relatively few studies that have ventured outside

the Big Five, Internet addiction has been positively associated with sensation seeking (Lin

& Tsai, 2002), and, within Cloninger’s tridimensional theory of personality, it is associated

with high Novelty Seeking, high Harm Avoidance, and low Reward Dependence (Ko et al.,

2006). Further research has implicated Loneliness as an important situational factor which

is firmly entrenched in aspects of personality (Hamburger & Ben-Artzi, 2003).

The present body of research on Internet overuse is insufficient for several reasons.

The first is not so much a criticism of the research as it is a difficulty of the field in


general: Internet research cannot help but be constantly outdated. The landscape is

vastly different now than it was two years ago, let alone ten. Any research attempting to

build upon the existing literature must take into account the fact that more, different

people use the Internet for more, different purposes now than in the past. The portrait

that previous research has painted of the typical Internet over-user as a socially inept,

adolescent male grows less and less convincing (though there is still probably some truth

to it). That is not to say that previous research was not thorough, or was flawed in some

way, but new research that draws too heavily on old conclusions is bound to miss

important features of the Internet’s constant evolution.

The second deficit is in some sense a product of the first difficulty: research has

typically only taken a top-down approach to the pathology of Internet addiction, which

was necessary during the early days of the field but may no longer be appropriate.

Specifically, these studies focused on a clinical population, either screened through an

Internet addiction scale or self-selected, then analyzed how these people use the Internet

and what their common personality traits are. They have concluded that Internet addicts

are most often a part of an online community, which is attractive to them due to their

social deficits offline. Research on personality and Loneliness (Hamburger & Ben-Artzi,

2003) has corroborated this story, and subsequent research on more general populations

has by and large only attempted to confirm these results. For these reasons I suggest that

the importance of the online community in problematic Internet use research has been

exaggerated, at least in the current environment. For one thing, it differs greatly from the

experience of the average Internet user, especially now that the term ”Internet user”

encompasses such a large and rapidly growing portion of the general population. Further,

the above anecdote is meant to convey that the social aspects of the Internet are not the

only way its use can become problematic. Other explanations may be equally valid; for

example, just as Loneliness is an important situational factor for problematic social uses


of the Internet, perhaps Boredom and amount of free time can help explain some of its

other problematic uses.

I use the word problematic above to underscore the third deficit in current research:

the focus on ”addiction” has been extremely confining, particularly when dealing with an

issue that could theoretically affect a large portion of the population in varying degrees.

Since any attempt to classify Internet addiction amounts to an arbitrary cutoff, and

researchers have yet to reach a consensus of where that cutoff should be placed, or even

what scale to use (Byun et al., 2009), it seems wiser to consider a continuum of Internet

overuse. This will allow us to study behavior that doesn’t meet the requirements of

full-fledged Internet addiction, but can still be problematic for a large number of people.

Related to the narrow focus of Internet addiction research is the limited number of

ways personality psychology has been applied to it. Whereas most research has focused on

high-order personality traits such as the Big Five, given the elegant neuropsychological

explanation for seeking outlined above, low-order trait theories seem promising. Despite

the wide gap in knowledge between neuroscience and personality psychology, fruitful

attempts have been made to link the two. Gray’s Reinforcement Sensitivity Theory

(RST), for example, postulates that personality stems from the biologically based

Behavioral Approach System (BAS), Behavioral Inhibition System (BIS), and

Fight-Flight-Freeze System (FFFS) (Corr, 2008). Sensitivities within these systems result

in the reinforcement of certain behaviors, which over time become the stable high-order

personality traits described by, for example, the Big Five. RST shares many similarities

with Cloninger’s tridimensional model, which maintains that Novelty Seeking, Harm

Avoidance, and Reward Dependence constitute the most basic dimensions of personality

(Cloninger, 1987); however, RST, which is based on animal studies, has a firmer biological

foundation and is supported by a wealth of empirical evidence (Corr, 2008). Though we

lack the sophistication to link the seeking mechanism to RST, RST is a promising avenue


for exploring Internet overuse, especially given the success that was had with Cloninger’s

tridimensional model.

The present research will attempt to improve upon these deficits. Given the

ever-changing nature of the Internet, this study will be relatively independent of past

conclusions to the point of being exploratory. It will accomplish this by surveying amount

and type of Internet use, how problematic it is (on a continuous scale), important

situational factors such as Loneliness and Boredom, and competing theories of personality

concurrently. I hypothesize that low-order traits will have more explanatory power than

high-order ones, and that Loneliness and Boredom will arise as important predictors of

behavior. I also predict that previous conclusions, such as the fact that people low in

Extraversion and low in Stability use the Internet more, will still be valid, but will only

account for a limited amount of people experiencing problematic Internet use.

Method

Participants

Subjects participating in the online survey followed a link on the Personality Project

website (http://personality-project.org), a resource for scientific literature on personality.

The only incentive for completing the survey was a chance to see one’s Big Five

personality score. As such, participants are in no way a representative sample, though

they are demographically diverse. Data from a total of 14,146 subjects are reported. Due

to an error in data collection, gender and age were not recorded for roughly 11,000

subjects. Of the 3,155 subjects for which gender and age were recorded, 64% were female,

with a median age of 24. This corresponds roughly to the demographics of previous

Personality Project research (70% female, median age 23), so we can safely assume the

sample is similar to what has been reported in the past (Revelle, Wilt, & Rosenthal, 2010).


Measures

Subjects were asked to report the average number of hours they spend online per

day. In light of research on motivations to use the Internet (Landers & Lounsbury, 2006;

Hamburger & Ben-Artzi, 2000), they were also asked to report how many hours they

spend doing the following: using the Internet for work or school; communicating with

others online (through chatting, e-mailing, sending messages through social networking

sites, etc.) (excluding for work or school); browsing social networking sites (e.g.,

Facebook, Myspace) or other online communities; browsing websites other than social

networking sites or other online communities (excluding for work or school); and playing

games online (including gambling). In order to further characterize social uses of the

Internet (communication and browsing social networking sites) subjects were asked what

proportion of the people they interact with online were first met online. They were also

asked how many times they check e-mail per day, how may websites they regularly check

for updates, and whether or not they own a smart phone. (See Appendix Table A1 for the

ranges and corresponding numerical values.)

To measure how problematic Internet use was for individuals, the Internet overuse

scale was developed based on existing Internet addiction and overuse scales (Caplan, 2002;

Morahan-Martin & Schumacher, 2000; Song, Larose, Eastin, & Lin, 2004). A total of 10

items were used. Items were chosen, reworded, or created in order to be as general as

possible (”My use of the Internet interferes with other activities”) in contrast to the

tendency of some existing scales to focus on social uses of the Internet. (See Table 1 for

the full list of questions comprising the scale.)

100 Big Five personality items, 20 for each factor, were selected from the

International Personality Item Pool (IPIP) developed by Goldberg (Goldberg et al., 2006).

The IPIP is a public domain equivalent to many commonly used, proprietary personality

scales. The items chosen were developed as markers of the Big Five personality


dimensions of Openness to Experience, Conscientiousness, Extraversion, Agreeableness,

and Stability (Goldberg, 1990, 1999).

An additional 36 items from the IPIP inventory were based on Carver and White’s

BIS and BAS scales (1994). BIS is measured by a single scale (”I am always worried

about something”), while BAS is divided into Fun-seeking (”I am willing to try anything

once”), Drive (”I take charge”), and Reward-responsiveness subscales (”I feel excited or

happy for no apparent reason”), which load onto a single BAS factor.

Four of twenty items from the Revised UCLA Loneliness Scale (Russell, Peplau, &

Cutrona, 1980) were presented to assess feelings of loneliness (”I feel isolated from

others”). To assess feelings of boredom and excessive free time, two items were taken from

the IPIP and two new items were created (”I often find myself with nothing to do”).

All Internet overuse and personality items were formatted as Likert-type

self-statements ranging from 1 (very inaccurate) to 6 (very accurate).

Procedure

The online survey made use of the Synthetic Aperture Personality Assessment

(SAPA) methodology (Revelle et al., 2010). SAPA takes its name from the Synthetic

Aperture Measurement technique used in astronomy, in which a large, high-resolution

radio telescope is created by synthesizing the input of many smaller ones. Similarly, using

SAPA, a survey with many items can be synthesized from many surveys with relatively

few items.

The method relies on a large sample size achieved via the Internet to essentially

spread out the burden placed on any one subject. Each participant answered roughly 85

questions from a pool of several hundred, for the purposes of this study and others being

run simultaneously through the Personality Project. The items were systematically

sampled so that every pair of items was presented an equal number of times, with the


exception that Big Five items were sampled considerably more in order to provide

accurate feedback to the subject. This allowed for statistics to be performed at the

correlation level.

The survey consisted of: 50 Big Five items (10 for each factor) sampled from 100

Big Five items (20 for each factor); 10 items sampled from the BIS/BAS, Internet overuse,

Loneliness, and Boredom scales (among others unrelated to this research); and all the

Internet usage items described above.

Subjects entering the online survey were greeted by a welcome screen and asked to

agree to a consent form. They then filled out basic demographic information (age, sex,

education, and country of residence). Subjects from the United States were asked their

ethnic identity, SAT Verbal and Quantitative scores, and ACT total score. They then

completed the survey. Upon completion, subjects were given feedback on their Big Five

personality scores, presented numerically, graphically, and in paragraph form.

Results

Descriptive statistics for demographic and Internet use and overuse items are

reported in Table 1. Internet overuse items had means ranging from 2.38 to 3.74 and

standard deviations ranging from 1.47 to 1.77, indicating a fairly large range of perception

of Internet use centered around neutral. With the median subject reporting using the

Internet 2 to 4 hours per day, the online sample does not appear to be grossly skewed

towards high Internet use, but presumably it is slightly higher than a representative

sample. The median subject uses the Internet for work or school 1-2 hours, and spends 1

hour or less on other uses. Subjects tended to interact more with people first met offline

than online. The median subject checks 3 websites for updates regularly, and checks his or

her e-mail 3 times per day. Slightly over half the sample (51%) owns a smart phone.

Descriptive statistics cannot be reported at the scale level because no one individual


answers all questions of a scale in the SAPA methodology.

Standardized Cronbach’s α scores for each scale are easily calculated directly from

the correlation matrix (Table 2). The Big Five scales all had excellent reliability, with α’s

ranging from 0.86 to 0.93. BIS and BAS both had good reliability (α = 0.87, 0.86),

although the BAS subscales were slightly less reliable. The Loneliness and Boredom scales

had mediocre reliability (α = 0.66, 0.74), which is to be expected with only 4 items each.

Signifance statistics are not reported for correlations or regressions because, with

such a large sample size, they would grow redundant. Instead, these results focus on effect

sizes.

The correlations among scales and the demographic and Internet usage items were

calculated from the raw, pairwise item correlations synthetically, taking into account that

scale by scale intercorrelations are just composites of correlation (Revelle et al., 2010) (see

Appendix Table A2). Of particular relevance to this study are the correlations of the

various personality scales to the Internet overuse scale and the Internet use items.

Internet overuse was correlated negatively with Conscientiousness (−0.45), Stability

(−0.28), Agreeableness (−0.21), and Extraversion (−0.20) and positively with BIS (0.33),

Boredom (0.32), BAS Fun-seeking (0.17), and Loneliness (0.13). Results are similar but to

a lesser degree for the Internet usage items; for example, total non-work or school Internet

use correlated negatively with Conscientousness (−0.27), Agreeableness (−0.19),

Extraversion (−0.17), and BAS Drive (−0.13), and positively with Boredom (0.30),

Loneliness (0.13), and BIS (0.11). Subjective Internet overuse was correlated with total

Internet use (0.42) but not with Internet use for work or school (0.01). As expected,

Loneliness correllated with Boredom (0.23).

The BIS/BAS scales were intimately related to the Big Five Scales. BIS was

extremely negatively correlated with Stability (−0.82). The BAS subscales, Fun-seeking,

Drive, and Reward-sensitivity, were all positively correlated with Extraversion (0.38, 0.59,


0.49) and Openness (0.20, 0.35, 0.13), while Fun-seeking was negatively correlated with

Conscientiousness (−0.24), Drive was positively correlated with Conscientiousness (0.25),

and Rewad-sensitivity was positively correlated with Agreeableness (0.52). Unsurprisingly,

BAS itself was positively correlated to Extraversion (0.63), Openness (0.31), and

Agreeableness (0.23).

Multiple regressions were performed from the correlation matrix (Revelle et al.,

2010). Table 3 reports the results of various models regressing Big Five or BIS/BAS scales

(with or without subscales), demographic variables, and Loneliness and Boredom scales on

Internet overuse in a hierarchical fashion, in order to show the differences in variance

explained by competing theories of personality and the contributions of demographics,

Loneliness, and Boredom. The models that included all three sets of variables had the

best fit, with the Big Five model (R2 = 0.286) beating out BIS/BAS with and without

subscales (R2 = 0.242, 0.221). In the BIS/BAS model with subscales, the best predictors

(those with the highest β weights) were BIS (β = 0.32), BAS Fun-seeking (0.22), Boredom

(0.21), and Age (−0.12). In the Big Five model, the best predictors were

Conscientiousness (β = −0.35), Stability (−0.17), Age (−0.15), and Openness (0.12).

Table 4 reports the results of similar regressions, this time on total hours spent online, not

for work or school. R2 values were lower for these models, but again the Big Five model

with demographics, Loneliness, and Boredom (R2 = 0.134) had better fit than either

BIS/BAS model (R2 = 0.118, 0.109). Boredom played a large role in this model

(β = 0.19), as did Conscientiousness (−0.16).

Since the Big Five model with demographics, Loneliness, and Boredom was

consistently the best model, it is the only one reported for the remaining Internet use

outcomes (Table 5). Gender (being female) played a role in predicting Internet use for

work or school (β = 0.15), using social networking sites (0.11), and interacting with people

met offline (0.11). Somewhat surprisingly, Extraversion positively predicted using the


Internet to communicate (0.09) and use social networking sites (0.10) and (less

surprisingly) predicted owning a smart phone (0.13). Lonely people were more likely to

interact with people met online (−0.18) (note the direction of the scale: lower values

indicate more people met online, higher values, more people met offline) but spent less

time using the Internet for communication (−0.07), social networking (−0.12), and gaming

(−0.13). Rather, Bored people spent more time using the Internet to communicate (0.14),

use social networking sites (0.17) and other websites (0.16), and play games (0.19).

To take a closer look at social uses of the Internet, models were extended to include

who one interacts with online and possession of a smart phone (Table 6). People who

interact more with people first met online tend to use the Internet more for

communication (β = −0.29), social networking (−0.21), and gaming (−0.25). People who

own a smart phone tend to check their e-mail more often (0.11).

Discussion

Use and overuse of the Internet cannot be explained in terms of social deficits alone.

The results of this study contrast the existing literature in many ways. While

Extraversion and Stability remain inversely related to Internet overuse, Conscientiousness

emerges as its strongest predictor. The positive association with Openness may imply that

the vast amount of information on the Internet appeals to people for intellectual reasons.

Interestingly, Loneliness predicts Internet overuse in the direction opposite of what was

expected when the effects of Boredom, a stronger but related predictor, are taken into

account through regression. The emerging picture is not of someone with social deficits

relying on the Internet for companionship, but of someone lacking impulse control and

drive, with a lot of free time on his or her hands.

The BIS/BAS scales were generally not as predictive as Big Five, but still bear

mentioning. The high predictive strength of BIS and BAS Fun Seeking on Internet overuse


paints an interesting picture: people who have a high desire for fun but are too inhibited

to express it in other areas of life resort to using the Internet excessively. However, the

extremely high negative correlation between BIS and Stability raises doubt as to whether

the two scales are indeed measuring different constructs. Once again, the data point to a

lack of impulse control, though itis difficult to interpret what exactly the BIS scale tells us.

The demographic variables reveal striking differences in how different people use the

Internet. Females tend to use the Internet more overall, for work or school, and for

browsing social networking sites, while males tend to use it more for online gaming.

Despite these differences, subjective Internet overuse does not vary with gender.

Unsurprisingly, older, more educated people tend to use the Internet more for work or

school and check their e-mail more often, while younger people tend to use social

networking sites more. These point to different motivations for using the Internet that

vary with age, gender, and educational attainment.

The fact that Boredom is better than Loneliness at predicting of Internet use across

the board, including social uses, is quite telling. It detracts from the hypothesis that

lonely people resort to the Internet for friends; rather, the Internet is something that

draws people with a lot of free time on their hands. Of course, the two concepts are

closely related, but in general the broader notion that Boredom leads to Internet use is

favored over the alternative of Loneliness, which is relegated to social uses of the Internet.

It is also true that people high in Extraversion tend to use the Internet more for chatting

and social networking; unsurprisingly, they carry their need for social interaction with

them online.

Not all of the data disconfirms the social deficit hypothesis; the fact that Loneliness

predicts more interaction with people met online lends credence to the idea that lonely

people socialize online. Further, people who interact with people met online tend to use

the Internet more overall and for social purposes; however, they are nto more likely to


beleive they are overusing the Internet. This raises the possibility that perhaps socializing

online can be healthy for some people who lack social skills, or at least preferable to the

alternative of isolation.

There are some important limitations to bear in mind when interpreting the results

of this study. First, the Loneliness and Boredom scales, which turned out to be associated

with Internet use and overuse in interesting ways, are only comprised of 4 items each, and

hence have relatively low reliabilty. Future studies should use longer, more reliable scales

to explore these relationships, given their theoretical importance. Second, the instrument

for measuring amount and type of Internet use could be improved upon. Although easy to

implement in survey form, it may have been difficult for subjects to give an accurate self

report of Internet use, especially if, for example, someone spends a few minutes chatting

here and there while using the Internet for work. Future studies could address this

problem by monitoring actual Internet use, and could look at other interesting outcomes,

like frequency of switching tasks while using the Internet.

Although our measures lack the specificity to say how ”compulsively” someone uses

the Internet, as we would like to have to address the role of the seeking mechanism in

Internet overuse, the results we do have, particularly the strong relationship between

overuse and low Conscientiousness, support the theoretical connection. People low in

Conscientiousness tend to let their Internet use get away from them. The association with

high Openness may indicate that this is especially true for people attuned to new

information. From a BIS/BAS approach, one would not have expected inhibition to be so

strongly associated with overuse (though the theoretical grounds of the BIS scale are

suspect), but the predictive power of BAS Fun-seeking fits in nicely with the concept of

seeking. It seems reasonable to conclude that the seeking mechanism plays a role in

Internet use and overuse, but it does so amidst many other motivations, like socializing,

from which it is difficult to disentangle at the personality level.


These results are not intended to disprove the previous hypothesis that socially

inept people turn to the Internet; with countless people using the Internet for myriad

reasons, there is undoubtedly some truth to this. Rather, it should be emphasized that

this explanation no longer generalizes to the Internet using population at large, which has

expanded considerably since much of the previous research was conducted. It is helpful to

take a fresh, unbiased look at human interactions with the Internet, a medium that for

good or ill plays an ever increasing role in our lives.


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Revelle, W., Wilt, J., & Rosenthal, A. (2010). Handbook of Individual Differences in

Cognition: Attention, Memory and Executive Control. In A. A. Gruszka,

G. Matthews, & B. Szymura (Eds.), (chap. Individual differences in cognition: New

methods for examining the personality-cognition link).

Russell, D., Peplau, L. A., & Cutrona, C. E. (1980). The Revised UCLA Loneliness Scale:

Concurrent and discriminant validity evidence. Journal of Personality and Social

Psychology , 39 (3), 472–480.

Song, I., Larose, R., Eastin, M. S., & Lin, C. A. (2004). Internet gratifications and

Internet addiction: On the uses and abuses of new media. CyberPsychology and

Behavior , 7 (4), 384–394.


Appendix

Additional Tables

Table A1

Table A2


Table 1

Descriptive statistics of demographic (for Gender, 1=Male, 2=Female), Internet overuse

(reverse coded items denoted (R)), and Internet use items (for Phone, 1=Does not own a

smart phone, 2=Does own a smart phone, see Appendix Table A1 for other values

n Mean SD Median

Gender 3155 1.64 0.48 2

Educ 11944 3.12 1.05 3

Age 3155 27.76 11.49 24

”I use the Internet so much that it interferes with other activities” 832 2.71 1.65 2

”I use the Internet without really thinking why” 849 3.47 1.72 4

”I never stay online longer than originally intended” (R) 853 2.46 1.47 2

”I have never lost sleep to spend more time online” (R) 821 3.02 1.77 3

”I lose track of time online” 831 3.74 1.66 4

”I rarely miss being online if I cant go on” (R) 870 3.65 1.66 4

”I have trouble completing other tasks when the Internet is accessible” 885 3.07 1.67 3

”Sometimes I would be better off not having access to the Internet” 887 2.99 1.71 3

”My performance at work or school has suffered due to my Internet use” 892 2.38 1.59 2

”I sometimes use the Internet when I should be spending time with

friends or family”

875 3.43 1.69 4

Total 11356 3.35 1.57 3

Work 11329 2.59 1.43 2

Chat 11286 1.82 1.37 1

Social 11300 1.72 1.36 1

Web 11317 1.56 1.25 1

Game 11236 0.72 1.24 0

SocialWho 8009 4.83 1.41 5

Updates 11119 2.9 1.87 3

Email 11374 3.33 2.63 3

Phone 11437 1.51 0.5 2

TotalNonwork 9923 1.06 1.19 1


Table 2

Standardized α’s, average item correlation, and number of items for each scale

Standardized α Average item correlation Number of items

Extra 0.93 0.40 20

Stab 0.92 0.38 20

Cons 0.92 0.35 20

Agree 0.90 0.31 20

Open 0.86 0.23 20

InternetOveruse 0.86 0.37 10

BIS 0.87 0.39 10

BASFun 0.81 0.30 10

BASDrive 0.79 0.27 10

BASReward 0.74 0.32 6

BAS 0.86 0.19 26

Lonely 0.66 0.39 4

Bored 0.74 0.41 4


Tab

le3

βw

eigh

tsan

dR

2va

lues

of

vari

ou

sli

nea

rre

gres

sion

mod

els

for

subj

ecti

veIn

tern

etove

ruse

.B

igF

ive

mod

els

are

com

pare

dto

BIS

/B

AS

(wit

han

dw

ithou

tsu

bsca

les)

,an

ddem

ogra

phic

,L

on

elin

ess,

an

dB

ore

dom

vari

abl

esare

added

tosh

ow

thei

rim

pact

on

perc

ent

vari

an

ceex

pla

ined

(R2.

Intern

etO

veru

se

Mod

el1

Mod

el2

Mod

el3

Mod

el4

Mod

el5

Model

6M

od

el7

Mod

el8

Mod

el9

Gen

der

-0.

010.

01-

-0.0

6-0

.04

--0

.10

-0.0

6

Age

--0

.17

-0.15

--0

.16

-0.12

--0

.19

-0.1

5

Ed

uc

-0.

040.

04-

0.03

0.04

-0.

040.

05

Extr

a-0

.07

-0.0

8-0

.09

--

--

--

Sta

b-0

.18

-0.1

7-0.17

--

--

--

Con

s-0

.40

-0.3

7-0.35

--

--

--

Agr

ee-0

.04

-0.0

3-0

.04

--

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en0.0

80.1

00.12

--

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--

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--

-0.

380.

360.32

0.35

0.34

0.29

BA

SF

un

--

-0.

310.

260.22

--

-

BA

SD

rive

--

--0

.05

-0.0

6-0

.01

--

-

BA

SR

eward

--

--0

.14

-0.1

2-0

.09

--

-

BA

S-

--

--

-0.

110.

080.

12

Lon

ely

--

-0.0

7-

-0.

02-

-0.

07

Bore

d-

-0.

10-

-0.21

--

0.23

R2

0.25

00.

276

0.286

0.17

850.

202

0.242

0.12

10.

162

0.221


Tab

le4

βw

eigh

tsan

dR

2va

lues

of

vari

ou

sli

nea

rre

gres

sion

mod

els

for

hou

rspe

rday

spen

tu

sin

gth

eIn

tern

et,

not

for

work

or

schoo

l.

Big

Fiv

em

odel

sare

com

pare

dto

BIS

/B

AS

(wit

han

dw

ithou

tsu

bsca

les)

,an

ddem

ogra

phic

,L

on

elin

ess,

an

dB

ore

dom

vari

abl

es

are

added

tosh

ow

thei

rim

pact

on

perc

ent

vari

an

ceex

pla

ined

(R2.

Tota

lNonwork

Mod

el1

Mod

el2

Mod

el3

Mod

el4

Model

5M

od

el6

Mod

el7

Mod

el8

Mod

el9

Gen

der

--0

.01

0.00

--0

.06

-0.0

3-

-0.0

9-0

.05

Age

--0

.07

-0.0

6-

-0.1

0-0

.07

--0

.12

-0.0

8

Ed

uc

--0

.06

-0.0

6-

-0.0

6-0

.05

--0

.06

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5

Extr

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--

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b0.

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02-

--

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-

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s-0

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-0.22

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--

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e-0

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--

--

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en0.0

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07-

--

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--

-0.

120.

110.

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110.

05

BA

SF

un

--

-0.

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09-

--

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SD

rive

--

--0

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--

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SR

ewar

d-

--

-0.1

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.11

-0.0

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--

BA

S-

--

--

--0

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-0.0

8-0

.03

Lon

ely

--

0.00

--

0.04

--

0.06

Bore

d-

-0.19

--

0.23

--

0.25

R2

0.09

50.

107

0.134

0.04

80.

068

0.118

0.01

40.

044

0.109


Tab

le5

βw

eigh

tsan

dR

2va

lues

of

dem

ogra

phic

s,B

igF

ive,

Lon

elin

ess,

an

dB

ore

dom

regr

esse

don

each

ou

tcom

eva

riabl

e.

Inte

rnet

Ove

ruse

Tot

al

Tota

lNon

wor

kW

ork

Ch

atS

oci

alS

oci

alW

ho

Web

Gam

eW

ebsi

tes

Em

ail

Phon

e

Gen

der

0.01

0.1

30.

000.15

0.07

0.10

0.11

0.00

-0.0

9-0

.08

0.00

0.01

Age

-0.1

50.0

4-0

.06

0.11

-0.0

3-0.18

-0.0

2-0

.03

0.01

-0.0

80.11

0.02

Ed

uc

0.0

40.0

4-0

.06

0.07

0.00

-0.0

20.

040.

01-0

.08

0.12

0.20

0.00

Extr

a-0

.09

-0.1

0-0

.07

-0.0

20.09

0.10

-0.0

3-0

.06

-0.0

8-0

.06

-0.0

40.13

Sta

b-0

.17

-0.0

20.0

2-0

.02

-0.0

5-0

.09

0.01

0.01

0.01

-0.0

1-0

.04

-0.0

2

Con

s-0

.35

-0.0

6-0

.16

0.07

-0.0

4-0

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0.01

-0.0

4-0

.05

-0.0

30.

010.

05

Agr

ee-0

.04

-0.0

9-0

.05

-0.0

3-0

.03

-0.0

20.

01-0

.04

-0.0

7-0

.03

-0.0

9-0

.05

Op

en0.

120.

150.0

70.

060.

01-0

.04

0.08

0.08

0.03

0.12

0.10

-0.0

4

Lon

ely

-0.0

7-0

.05

0.0

0-0

.01

-0.07

-0.11

-0.18

0.05

-0.13

0.06

-0.0

7-0

.02

Bore

d0.1

00.0

80.1

9-0

.09

0.14

0.17

-0.0

20.16

0.19

0.08

-0.0

5-0

.03

R2

0.2

860.0

57

0.1

340.

077

0.04

20.

125

0.05

70.

062

0.08

50.

067

0.08

70.

021


Table 6

β weights and R2 values of demographics, Big Five, Loneliness, Boredom, interacting with

people first met online, and smart phone ownership regressed on each Internet overuse and

social uses of the Internet.

InternetOveruse Chat Social Game Email

Gender 0.01 0.10 0.13 -0.07 0.00

Age -0.15 -0.04 -0.19 0.00 0.11

Educ 0.04 0.01 -0.01 -0.07 0.20

Extra -0.09 0.07 0.09 -0.09 -0.05

Stab -0.17 -0.04 -0.08 0.02 -0.04

Cons -0.35 -0.04 -0.03 -0.05 0.01

Agree -0.04 -0.03 -0.01 -0.06 -0.08

Open 0.12 0.03 -0.02 0.05 0.10

Lonely -0.07 -0.13 -0.15 -0.18 -0.06

Bored 0.10 0.14 0.16 0.18 -0.05

SocialWho 0.00 -0.29 -0.21 -0.25 0.01

Phone -0.01 0.04 0.05 0.02 0.11

R2 0.29 0.12 0.17 0.15 0.10


Tab

leA

1

Ord

inal

ran

ges

for

Edu

cati

on

an

dIn

tern

etu

sage

item

s.

Valu

eE

duc

Socia

lWho

Tota

l,W

ork

,C

hat,

Socia

l,W

eb,

Gam

es

Websi

tes

Em

ail

(Level

of

educati

on)

(Avera

ge

hours

per

day)

(Wit

hw

hom

do

you

inte

ract

online?)

(Num

ber

of

websi

tes

you

regula

rly

check

for

up

date

s)

(Avera

ge

tim

es

checked

per

day)

NA

Not

applicable

0L

ess

than

12

years

00

00

1H

igh

school

gra

duate

Only

people

firs

tm

et

online

less

than

11

1

2Som

ecollege,

did

not

gra

duate

Most

lyp

eople

firs

tm

et

online

at

least

1but

less

than

22

2

3C

urr

entl

yatt

endin

gcollege

Slightl

ym

ore

people

firs

tm

et

online

than

offl

ine

at

least

2but

less

than

43

3

4C

ollege

gra

duate

Slightl

ym

ore

people

firs

tm

et

offl

ine

than

online

at

least

4but

less

than

64

4

5G

raduate

or

pro

fess

ional

degre

eM

ost

lyp

eople

firs

tm

et

offl

ine

at

least

6but

less

than

85

5

6O

nly

people

firs

tm

et

offl

ine

at

least

8but

less

than

10

6-8

6-1

0

7at

least

10

but

less

than

12

9-1

111-1

5

8at

least

12

but

less

than

14

12-1

416-2

0

9m

ore

than

14

14+

21-2

5

10

26-3

0

11

31+

Internet Overuse and Personality 28T

ab

leA

2

Raw

corr

elati

on

sof

dem

ogra

phic

s,sc

ale

s,an

dIn

tern

etu

seit

ems

Gender

Educ

Age

Extra

Stab

Cons

Agree

Open

InternetOveruse

BIS

BASFun

BASDrive

BASReward

BAS

Lonely

Bored

Total

Work

Chat

Social

Web

Game

SocialWho

Websites

Emai l

Phone

TotalNonwork

Gender

1.0

0-

--

--

--

--

--

--

--

--

--

--

--

--

-

Educ

-0.0

11.0

0-

--

--

--

--

--

--

--

--

--

--

--

--

Age

0.0

10.3

31.0

0-

--

--

--

--

--

--

--

--

--

--

--

-

Extr

a0.0

10.0

00.0

31.0

0-

--

--

--

--

--

--

--

--

--

--

--

Sta

b-0

.22

0.0

40.0

90.3

11.0

0-

--

--

--

--

--

--

--

--

--

--

-

Cons

0.0

90.0

50.2

00.1

90.2

11.0

0-

--

--

--

--

--

--

--

--

--

--

Agre

e0.2

50.0

20.1

30.4

20.2

00.3

01.0

0-

--

--

--

--

--

--

--

--

--

-

Op

en

-0.0

90.1

50.1

60.2

40.1

80.1

40.2

21.0

0-

--

--

--

--

--

--

--

--

--

Inte

rnetO

veru

se-0

.01

-0.0

3-0

.24

-0.2

0-0

.28

-0.4

5-0

.21

-0.0

31.0

0-

--

--

--

--

--

--

--

--

-

BIS

0.2

8-0

.01

-0.1

5-0

.37

-0.8

2-0

.21

-0.0

1-0

.23

0.3

31.0

0-

--

--

--

--

--

--

--

--

BA

SFun

-0.1

1-0

.10

-0.2

40.3

80.0

5-0

.24

0.0

20.2

00.1

7-0

.15

1.0

0-

--

--

--

--

--

--

--

-

BA

SD

rive

-0.0

2-0

.03

-0.1

20.5

90.1

10.2

50.1

10.3

5-0

.06

-0.2

20.3

91.0

0-

--

--

--

--

--

--

--

BA

SR

ew

ard

0.1

8-0

.12

-0.1

20.4

9-0

.01

0.0

30.5

20.1

30.0

10.1

00.4

20.3

21.0

0-

--

--

--

--

--

--

-

BA

S-0

.01

-0.1

0-0

.21

0.6

30.0

80.0

10.2

30.3

10.0

6-0

.15

0.8

20.7

70.6

81.0

0-

--

--

--

--

--

--

Lonely

-0.0

50.0

40.0

9-0

.50

-0.2

9-0

.21

-0.4

70.0

00.1

30.1

8-0

.12

-0.1

8-0

.39

-0.2

71.0

0-

--

--

--

--

--

-

Bore

d-0

.09

-0.0

7-0

.19

-0.3

2-0

.25

-0.4

1-0

.34

-0.1

60.3

20.1

90.0

4-0

.24

-0.0

8-0

.12

0.2

31.0

0-

--

--

--

--

--

Tota

l0.0

90.0

60.0

3-0

.11

-0.0

8-0

.09

-0.0

80.0

80.4

20.1

0-0

.04

-0.0

6-0

.08

-0.0

80.0

70.1

11.0

0-

--

--

--

--

-

Work

0.1

60.1

20.1

70.0

3-0

.00

0.1

40.0

80.0

80.0

1-0

.00

-0.1

20.0

2-0

.05

-0.0

6-0

.01

-0.1

50.5

61.0

0-

--

--

--

--

Chat

0.0

6-0

.03

-0.0

80.0

4-0

.07

-0.0

9-0

.02

-0.0

30.3

40.1

10.0

70.0

30.0

70.0

8-0

.05

0.1

40.4

80.2

91.0

0-

--

--

--

-

Socia

l0.1

1-0

.11

-0.2

50.0

5-0

.12

-0.1

1-0

.01

-0.1

10.3

80.1

80.0

90.0

30.1

00.0

9-0

.11

0.1

80.4

10.1

90.6

21.0

0-

--

--

--

Web

-0.0

40.0

0-0

.05

-0.1

4-0

.07

-0.1

3-0

.14

0.0

20.2

90.1

1-0

.03

-0.0

9-0

.11

-0.0

90.1

40.2

10.4

50.2

60.4

40.3

81.0

0-

--

--

-

Gam

e-0

.13

-0.0

9-0

.08

-0.1

0-0

.02

-0.1

4-0

.13

-0.0

40.2

00.0

10.0

9-0

.07

-0.0

3-0

.00

-0.0

10.2

30.3

00.0

80.3

10.2

60.3

21.0

0-

--

--

Socia

lWho

0.1

10.0

40.0

00.1

00.0

60.0

80.1

40.0

8-0

.04

-0.0

40.0

40.0

50.1

30.0

9-0

.19

-0.0

9-0

.16

-0.1

0-0

.27

-0.1

9-0

.26

-0.2

61.0

0-

--

-

Websi

tes

-0.1

10.1

1-0

.04

-0.1

1-0

.04

-0.1

0-0

.12

0.0

90.3

20.0

8-0

.01

-0.0

1-0

.07

-0.0

30.1

30.1

30.3

00.1

20.2

40.2

10.3

50.1

6-0

.15

1.0

0-

--

Em

ail

-0.0

10.2

50.1

8-0

.01

0.0

00.0

5-0

.02

0.1

20.1

80.0

2-0

.09

0.0

6-0

.06

-0.0

30.0

1-0

.07

0.3

60.3

00.2

30.1

20.1

80.0

00.0

20.2

81.0

0-

-

Phone

0.0

10.0

00.0

20.1

20.0

30.0

70.0

3-0

.01

-0.0

6-0

.04

-0.0

20.0

9-0

.04

0.0

2-0

.07

-0.0

70.0

30.0

90.0

50.0

60.0

10.0

1-0

.02

0.0

00.1

11.0

0-

Tota

lNonw

ork

-0.0

6-0

.09

-0.1

4-0

.17

-0.0

9-0

.27

-0.1

9-0

.03

0.4

30.1

10.0

5-0

.13

-0.0

8-0

.06

0.1

30.3

00.5

3-0

.27

0.3

20.3

40.3

30.3

5-0

.17

0.2

20.0

7-0

.04

1.0

0

internet overuse and personality 1 running head: internet overuse

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