Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
Online Social Network Dependency:
Theoretical Development and Testing of Competing Models
Dimple R. Thadani
Department of Information Systems
City University of Hong Kong
[email protected]
Abstract
The proliferation of new social media
technologies has changed the behavioral patterns of
online users. This study aims at investigating the
structure and dimensionality of the Online Social
Network Dependency (OSN Dependency). We tested
the competing models built upon the cognitivebehavioral model and the biopsychogical framework
of addiction. Our findings suggested that OSN
Dependency can be explained by a higher-order
factor model with seven first-order factors (i.e. mood
alternation, social benefit, negative outcomes,
compulsivity, excessive time, withdrawal, and
interpersonal control) and two correlated secondorder factors (i.e. social components and
intrapersonal components). The model provides a
good-fit to the data, reflecting logical consistency.
Implications of the current investigation for practice
and research are provided.
1. Introduction
With the advancement of Internet technologies,
there has been a rapid emergence of online
interactions between groups of people who share
similar interests, though they are congregated in an
absolute space [1]. A number of websites (e.g.
Facebook, Twitter, Myspace) have implemented
dynamic social contents in which online communities
can be built and sustained easily through the
facilitation of social connections and communications
between users. Evidence of this trend can be seen by
industrial reports from agencies that monitor the
activities of online users. In 2009, it was reported that
an average social-network user around the world spent
more than five and a half hours per month on social
networking sites, which was triple the time spent on
other online activities, such as web browsing. From
April 2008 to April 2009, the total minutes spent on
Facebook in U.S., in particular, has increased from 1.7
billion minutes to 13.9 billion minutes (700% annual
growth)[2].
Christy M.K. Cheung
Department of Finance & Decision Sciences
Hong Kong Baptist University
[email protected]
Alongside these figures, research studies show that
sociability of the Internet is responsible for the
excessive amounts of time individuals spend having
interactions via forums, online games, and blogs [3.4].
In other words, the recent emergence of these new
social media technologies has changed the concept of
the Internet as well as its usage. As a result, findings
from prior studies on internet dependencies, excessive
use or addictions may not be valid in this new context.
Thus, there is a need to investigate the concept of the
technological dependency under this new context.
The purpose of this study is thus to investigate the
dimensionality and the multi-faceted structure of the
Online Social Network (OSN) Dependency through the
examination of several competing models. The results
of this study are anticipated to increase our
understanding on the OSN Dependency, thereby
providing a solid foundation for the development of a
validated and robust instrument for measuring Online
Social Network Dependency.
2. Theoretical Background
Internet dependency or addiction has been
extensively studied from diverse disciplines including
the discipline of psychology, Information Systems, and
Education. Over the brief academic history of Internet
dependency, one of the most challenging tasks has
been to arrive at a universally accepted definition of
the concept [4]. There are as many as seven different
terms associated with the concept of Internet
dependency,
including
“Internet
Addiction”,
“Excessive Internet Use”, “Compulsive Internet Use”,
“Problematic Internet Use”, “Pathological Internet
Use”, “Cyberspace Addiction”, “Online Addiction” [4,
5]. We will use the term “dependency”, “addiction”
and “problematic use” interchangeably in this paper.
[6] is the first person who coined a term to describe
the concept of addiction. Internet addiction is thus
considered to be a kind of technological addiction, and
one in a subset of behavioral addiction [7].
1530-1605/11 $26.00 © 2011 IEEE
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Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
In this study, Online Social Network Dependency
(OSN Dependency) is defined as an impulse control
disorder in which an individual experiences rising
tension or arousal before engaging in online social
networking sites and a sense of relief or pleasure after
completion of the behavior. The OSN Dependency is
considered to be a form of internet addiction.
[8] developed a 29-item instrument that measures
the seven components of Internet problematic use
(GPIUS – Generalized problematic Internet use scale),
namely, mood alternation, social benefit, negative
outcomes, compulsivity, excessive time, withdrawal,
and interpersonal control. This instrument was derived
from [9] cognitive-behavioral theory of Internet
addiction. GPIUS was one of the best known and
frequently employed measurements for Internet
addiction [10]. A confirmatory factor analysis of the
GPIUS factor structure was performed by [11]. They
also assessed the psychometric properties of the
factors and items. Their results provided strong
support for the instrument.
One of the differences between the traditional
internet usage and the new form of internet usage is
that the latter involves more social interactions and
engagements. Hence the social components are
important in determining the degree of technological
tendency. In this regard, [12] proposed a
biopsychosocial component model of technological
addiction, which argues that addiction is in fact a
biopsychosocial process. In other words, online social
network dependency could be analyzed by identifying
and highlighting the social dimension.
Table 1: Dimensions of online social network
dependency
Dimension
Mood
Alternation
Definitions
Facilitation of the change of mood
/arousal or discomfort with the use of the
Social networking sites
Social
Perceived social benefits that could
Benefit
obtain from the social networking sites
Negative
Negative outcome associated with the
Outcome
use of social networking sites
An inability to control, reduce, or stop
Compulsivity online behavior, along with feelings of
guilt about time spent on social
networking sites
The degree to which an individual feels
Excessive
that he or she spends an excessive
Time
amount of time on social networking sites
or even loses track of time.
Withdrawal Difficulties with staying away from the
Social Networking Sites.
Interpersonal The ways in which individuals perceive
Control
increased social control when interacting
with others on the social networking sites
3. Factor Structure for Online Social
Network Dependency
[8] developed a 29-item instrument based on the
cognitive-behavioral theory of addiction [9].
Components of cognition, behaviors, affection in social
and intrapersonal levels were presented in the form of
seven dimensions (See Table 1).
4. Competing Model for Online Social
Network (OSN) Dependency
In this study, five alternative factor structures of
OSN Dependency with 26 observable items were
tested with reference to the approach used by [13].
Model 1 to Model 3 represented the non-hierarchical
structure with only first-order factors, whereas Model 4
and Model 5 represented the hierarchical structure with
more than one level of abstraction.
Model 1 was a first-order factor model. One factor
(OSN Dependency) is hypothesized to be accounted
for all common variances among the 26 observable
variables.
Model 2 was also a first-order factor model. It
hypothesized seven orthogonal/ uncorrelated first-order
factors (i.e. mood alternation, social benefit, negative
outcomes, compulsivity, excessive time, withdrawal,
and interpersonal control).
[8] performed an
exploratory factor analysis which resulted in seven
factors. Thus, Model 2 is considered to be a plausible
alternative model of underlying data structure.
Model 3 was a first-order factor model with seven
factors correlated with each other to represent different
dimensions of OSN Dependency.
Model 4 was a second-order factor model that
hypothesized seven first-order factors and two
orthogonal/uncorrelated second-order factors (i.e.
social components and intrapersonal components).
Model 5 assumes that the two second-order factors
in Model 4 were correlated.
5. Research Method
The sections below describe the details of data
collection procedure, measurement, data analytical
approach, and model competing criteria.
5.1. Data Collection
Facebook (www.facebook.com), an online social
networking site, was used in this study. We believe that
Facebook is appropriate for the current study due to the
surge of its popularity globally. Facebook has
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Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
surpassed MySpace and become the most popular
social networking site.
(http://blog.compete.com/2009/02/09/facebook-myspacetwitter-social-network/
.
A web-based field survey was used to test and
validate the conceptual model. A convenience sample
of Facebook users was created by inviting volunteers
to participate in our study. An invitation message
with the URL to the online questionnaire was posted
on a number of platforms including Facebook,
MySpace, MSN, and weblogs. A screening question
was used to ensure that the respondents were current
active users of Facebook. A total of 406 usable
questionnaires were collected. Among the respondents,
50.2% were female and 49.8% were male.
Table 2: Criteria and recommendation of fit indices
Fit Indices
Absolute fit
Interpretation
Goodness-of-fit index
(GFI)
Root mean square
residual (RMR)
Root mean square error of
approximation (RMSEA)
Incremental fit
Values close to 0.9
reflects a good fit
Value below 0.05
reflects a good fit
Value below 0.1 reflects
an acceptable fit
Adjusted good of fit index
(AGFI)
Normed fit index (NFI)
Values close to 0.9
reflects a good fit
Values close to 0.9
reflects a good fit
5.2. Measurement
Table 2 lists the measures used in the study.
Basically, we borrowed the measures from [8]’s
Generalized Problematic Internet Use Scale but we
modified the wordings to fit them to the context of
social networking sites instead of the context of the
Internet in general. The measurements employed a
seven-point Likert scale, from “1= strongly disagree”
to “7= strongly agree”.
5.3. Data Analytical Approach
The proposed factor structures were examined
through the LISREL 8.8 framework. LISREL is one
of the most widely used Structural Equation Modeling
(SEM) Techniques in the IT/IS literature [14]. If SEM
is appropriately applied, it can even surpass the firstgeneration techniques such as factor analysis,
discriminant analysis, multiple regression or principle
components analysis [15]. Particularly, SEM provides
a greater flexibility in estimating relationships among
multiple predictors and dependent variables with the
function of purification for measurement items of
significant variables [14]. It allows modeling with
unobservable latent variables, and it estimates the
model uncontaminated with measurement errors.
As suggested by [12], competing models should be
specified based on theory, logic, and prior studies. The
LISREL framework offers us a systematic approach to
statistically compare the theoretical models using the
goodness-of-fit indices. The best model is then selected
as representing the factor structure and dimensionality
of OSN Dependency in the sample data. Further, the
psychometric properties (i.e. validity and reliability) of
the selected model are examined.
5.4. Criteria for Comparing Model Data Fit
The determination of model fit in structural
equation modeling is usually based on several tests and
indicators. Chi-square test is very important as it is the
only statistical test that identifies a correct model given
the sample data. In contrast to traditional significance
testing, researchers are also interested in obtaining the
non-significant chi-square because it is a good
indicator in determining if the predicted model is
congruent with the observed data. Another alternative
is the ratio of the chi-square to the degrees of freedom
(Normed Chi-Square). Researchers have recommended
the ratio ranging from 2 to 5 as a reasonable fit for
Normed Chi-Square [16, 17].
In IS research, absolute fit indices and incremental
fit indices are the two most widely used measures to
determine how well the data fits the proposed model
[18]. For instance, [13] used absolute fit indices,
including the Goodness-of-Fit Index (GFI) and the
Root Mean Square Residual (RMSR) or Root Mean
Square Error of Approximation (RMSEA), to evaluate
individual models. They also used incremental fit
indices, including the Normed Fit Index (NFI) and the
Adjusted Goodness-of-Fit Index (AGFI), to reflect the
improvement in fit of one model over an alternative.
Some researchers [17, 19] provided the criteria and
interpretation of these measures (See table 2).
6. Results
The models are analyzed by confirmatory factor
analysis (CFA) using the maximum likelihood
estimation.
6.1. Multivariate Normality
Multivariate Normality, an important assumption of
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Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
Table 3: Model fit test results of initial and revised model
Indices
Chi-square (df)
Threshold
Smaller the better
Initial Model
(29 Items)
1358.2 (356)
First revised
Model (28 items)
1112.57 (329)
3.38
Second revised Third revised Model
Model (27 items)
(26 items)
928.08 (303)
750.4 (278)
Normed chisquare
GFI
2 to 5, less than 3
indicates a good fit
Around 0.9 or larger
3.83
0.81
0.84
0.85
0.88
RMR
RMSEA
AGFI
NFI
AIC
Less than 0.05
Less than 0.1
Around 0.9 or larger
around 0.9 or larger
Smaller the better
0.14
0.108
0.77
0.98
1516.52
0.13
0.077
0.80
0.98
1266.57
0.13
0.071
0.82
0.98
1078.08
0.040
0.065
0.84
0.98
896.4
confirmatory factor analysis, was tested by
examining the skewness and kurtosis of each scale.
Skewness refers to the measure of the degree of
asymmetry of data distribution, whereas kurtosis
refers to the measure of whether the data distribution
is peaked or flat relative to a normal distribution. The
descriptive analysis revealed that skewness for scale
items ranged between 0.096 and 0.846 and kurtosis
ranged from -0.983 to -0.446 which were within the
robustness threshold (+/-1) for normality as
suggested by [16].
6.2. Model Respecification
A measurement model of seven first-order factors
was identified. However, the hypothesized sevenfactor model (in the context of online social
networking sites) turned out to be a poor model. As
shown in table 3. most of the important fit indices (i.e.
normed chi-square, RMR, RMSEA, AGFI) of the
initial model were below the recommended threshold.
Therefore, as recommended by [20] there was a need
to re-specify the hypothesized model so as to detect
the ill-fitting parameters and achieve a clearer factor
structure.
Taking into consideration the empirical and
theoretical rationale of each step by considering the
modification index [21] and standardized factor
loadings [22], several models were re-specified (See
table 3). As suggested by [19], one item was dropped
at a time so as to avoid over modification. At the end
of the re-specification process, three items were
dropped. Table 4 shows the final 26-item model
which had acceptable fit indices (22/d.f = 2.70, GFI =
0.88, RMR = 0.040, RMSEA = 0.65, AGFI = 0.84;
NFI= 0.98). The AIC (Akaike Information Criterion)
value for the final model is the smallest, suggesting
that the final model is the most parsimonious model
among all models in the re-specification process.
3.06
2.70
6.3. Model Estimation
Competing model analyses were then performed.
Following [13], five plausible alternative models
were specified. The specification of these models
included fixing one of the paths from each of the
seven primary factors at 1.0 and the factor variance
for the higher-order factor at 1.0. Model 1 is a onefactor, 26-item model. The factor variance for the
single first-order factor (OSN Dependency) was fixed
at 1.0, allowing 26 observable variables to be free.
For Model 2, the 26 items were loaded onto seven
uncorrelated factors (i.e. mood alternation, social
benefit, negative outcomes, compulsivity, excessive
time, withdrawal, and interpersonal control) by
setting the covariances among the seven first-order
factors to be zero. For Model 3, the 26 items were
loaded onto seven correlated factors. Model 4
comprised a second-order factor (i.e. social
Component and intrapersonal component) onto which
the seven first-order factors (i.e. mood alternation,
social benefit, negative outcomes, compulsivity,
excessive time, withdrawal, and interpersonal control)
were loaded by fixing the covariances between social
component and intrapersonal component to be zero.
For Model 5, the first path for each of the two
second-order factors (i.e. social component and
intrapersonal component) was fixed to 1.0. For all
five models, the number of available data points was
equal to 26*(26+1)/2 = 351.
For Model 1, there were 52 free parameters that
included 26 error variances for the measured
variables and 26 factor loadings. This left (351-26) =
299 degrees of freedom for Model 1. There were 52
free parameters for Model 2 which included 26 error
variables, a total of (26-7) = 19 factor loadings, and 7
first-order factor variances, which resulted in 299
degrees of freedom. For Model 3, there were 52 free
parameters, 26 error variables, 19 factor loadings, 21
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Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
Table 4: List of measures used in this study
Dimensions
Mood
Alternation
Items
MA1 I use Facebook to talk with others
when I feel isolated
MA2 I use Facebook to make myself
feel better when I am down
MA3 I go on Facebook to make myself
feel better when I am anxious
Dimensions
ET1
Excessive
Time
SB1 I am treated better on Facebook
than in face-to-face relationships
Social
Benefit
Negative
Outcome
SB2 I feel safer relating to others on
Facebook rather than face-to-face
SB3 I am more confident socializing on
Facebook than offline
SB4 I am more comfortable with people
on Facebook than people in faceto-face relationships
SB5 I am treated better on Facebook
than offline
NO1 I have gotten into trouble at work
or school because of being on
Facebook
NO2 I have missed class or work
because I was on Facebook
NO3 I feel worthless offline, but I am
someone on Facebook
NO4 I have missed social event
because of being on Facebook
COMP I am unable to reduce time on
Facebook
Compulsivity
Items
I have lost track of time when I am
on Facebook
ET2 I have used Facebook for longer
time than I had expected to
ET3 I have spent a good deal of time on
Facebook
ET4 I have gone on Facebook for longer
time than I had intended
When I am not on Facebook, I
WITH1 wonder what is happening on
Facebook
WITH2 I feel lost if I cannot go on Facebook
Withdrawal
WITH3
IC1
IC2
Interpersonal
Control
IC3
It is hard to stop thinking about what
is waiting for me on Facebook
When I am on Facebook, I socialize
with other people without worrying
about how I look.
When I am on Facebook, I socialize
with other people without worrying
about the relationship commitment
I have control over how others
perceive me on Facebook
COMP I feel guilty about the amount of
time I spend on Facebook
COMP I have tried to stop using Facebook
for long periods of time
COMP I
have
made
unsuccessful
attempts to control my use of
Facebook
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Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
covariances among the seven first-order factors, 7
first-order factor variances, which resulted in 278
degrees of freedom. For Model 4, there are 52 free
parameters that included 26 error variances for
measured variables, 19 first-order factor loadings, 4
second-factor loadings, 7 first-order factor variances.
This left 295 degrees of freedom. Finally, there were
52 free parameters for Model 5, including 26 error
variances for measured variables, 19 first-order factor
loadings, 4 second-order factor loadings, 6 primary
factor disturbances, and 2 second-order factor
variances, and 1 covariance between second-order
factors. This provided 292 degrees of freedom.
14.4
0.18
0.55
0.93
By comparing the goodness-of-fit-indices, we
discovered that Model 3 performed the best.
However, it should be noted that the goodness-of-fit
of the higher order model would never surpass that of
the corresponding first-order model because the
higher order factor merely explains the covariation
among lower order factors in a more parsimonious
way [13, 23]. Thus, additional guidelines are
important for the model selection. As suggested by
Harlow and Newcomb (1990), model selection
should be based on four critical criteria – (1) logical
consistency, (2) empirical adequacy, (3) the ability to
capture most of the essential relationships among the
variables, and (4) Simplicity. In that case, based on
the above criteria, only two models, namely Model 3
and Model 5, should be retained. Indeed, Model 5 is
more theoretically valid and it is the most appropriate
model to capture the structure of OSN Dependency.
Furthermore, as described in Figure 1 (in appendix),
the paths from the second-order factors to the firstorder factors were all significant. Each of the factor
loadings were large and highly significant.
We believed that a third-order factor model with
seven first-order factors (i.e. mood alternation, social
benefit, negative outcomes, compulsivity, excessive
time, withdrawal, and interpersonal control), two
second-order factors (i.e. social component and
intrapersonal component) and one third-order factor
(OSN Dependency) might offer a better explanation
of the underlying structure of the online social
network addiction. However, the model could not be
identified and estimated as there must be at least 3
second-order factors in order for the third-factor
model to be identified [25].
2.70
0.065 0.88 0.040 0.84
0.98
6.5. Psychometric Properties
4.55
0.084 0.82 0.36
0.79
0.97
3.46
0.078 0.84 0.02
0.81
0.97
6.4. Goodness-of-Fit
Table 5 compares the fit indices of five alternative
models. Chi-square statistics of all models were
statistically significant with p-value<0.0001.
Model 1 to 3 represented the first-order factor
models. Model 1 and 2 provided a poor fit to the data.
Though Model 2 demonstrated a slight improvement
over Model 1, neither of them indicated a reasonable
fit with the data. Model 3 provided substantial
improvement over Model 2 and a good fit to the data
with desirable goodness-of-fit indices. The AGFI
index increased significantly from 0.47 (Model 2) to
0.84 (Model 3).
Table 5: Goodness of fit indices for competing
models (n=406)
Model
Normed
Incremental
Absolute fit
chifit
measures
square
measures
(chisquare/ RMSEA GFI RMR AGFI NFI
d.f)
1 4348.84 14.5
0.18 0.55 0.080 0.47 0.93
(299)
ChiSquare
(df)
2 4308.93
(299)
3 750.40
(278)
4 1343.40
(295)
5 1010.80
(292)
4: 0.82; Model 5: 0.84), and a lower value of normed
chi-square (Model 4: 3.46; Model 5: 4.55). Model 5
provided a substantial improvement over Model 4
with AGFI index improved from 0.79 (Model 4) to
0.81 (Model 5).
0.55 0.48
Model 4 and Model 5 were the second-order
factor models. The two models provide reasonable
model-data-fit and their fit indices were very close to
the recommended level. Comparing Model 4 and
Model 5, Model 5 performed slightly better than
Model 4, with a slightly higher value of GFI (Model
The psychometric model was then examined.
Table 6 presents the statistical significance of the
estimated loadings, the corresponding t-values, and
R-square values for the 26 observed variables. All
items presented significant factor loadings with tvalue larger than 2.00. Researchers suggested that a
loading of 0.70 to latent variable is considered to be a
high loading [26]. In our study, all items have high
factor loading (0.71 or above) with R-square of 0.51
or above, meaning that the items explained at least 51
percent of the variance in the construct.
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Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
Furthermore, as suggested by [16], the reliability
and validity of the measurement scale was assessed
through the investigation of Composite reliability
(CR) and average variance extracted (AVE). A
composite reliability of 0.70 or above and an average
variance extracted of more than 0.50 are deemed
acceptable. As shown in Table 6, the composite
reliability ranged from 0.76 to 0.95 and the average
Table 6: Parameter estimates for model 5
Latent
Observed Factor
Variable
Variable Loading t-value
MA1
0.71
13.25
Mood
Alternation
MA2
0.91
17.92
CR= 0.92
MA3
0.95
18.26
AVE= 0.77
SB1
0.84
12.08
Social Benefit
CR=0.90
SB2
0.87
22.73
AVE= 0.72
SB3
0.87
22.77
SB4
0.85
21.69
SB5
0.85
21.88
Negative
NO1
0.85
11.49
Outcome
NO2
0.88
22.62
CR= 0.88
NO3
0.84
21.26
AVE= 0.72
NO4
0.88
22.55
COMP1
0.83
11.33
Compulsivity
CR=0.77
COMP2
0.80
18.83
AVE= 0.76
COMP3
0.69
15.34
COMP4
0.87
21.47
ET1
0.86
9.95
Excessive
Time
ET2
0.87
22.47
CR=0.76
ET3
0.63
13.79
AVE=0.69
ET4
0.81
19.93
Withdrawal
WITH1
0.72
12.84
CR=0.95
WITH2
0.87
16.93
AVE=0.74
WITH3
0.92
17.69
Interpersonal
IC1
0.84
9.32
Control
IC2
0.89
19.98
CR=0.90
IC3
0.72
15.73
AVE=0.76
Rsquare
0.51
0.84
0.90
0.71
0.76
0.76
0.72
0.73
0.71
0.77
0.71
0.77
0.69
0.64
0.48
0.76
0.75
0.76
0.39
0.65
0.52
0.75
0.85
0.71
0.78
0.51
variance extracted ranged from 0.69 to 0.77. Hence,
all the measures selected have desirable psychometric
properties.
7. Discussion and Conclusion
The results suggested that OSN Dependency can
be assessed by a large number of highly related
factors. The second-order factor model (Model 5)
with seven first-order factors (i.e. mood alternation,
social benefit, negative outcomes, compulsivity,
excessive time, withdrawal, and interpersonal control)
and two correlated second-order factors (i.e. social
component and intrapersonal component) provides a
good-fit to the data, reflecting the logical formal
consistency.
7.1. Managerial and Research Implications
Understanding the OSN Dependency is
particularly important because social networking sites
have become an integral part of our daily life. In the
current study, our higher-order factor model can
assist web designers in understanding to what degree
the users are engaged socially in a particular website.
In addition, the model can also assist educators in
evaluating the extent to which a student is dependent
on the online social network. Essentially, the model
can help explain (1) what defines OSN Dependency
(2) basic components of OSN Dependency (3) which
attributes are relatively important to the formation of
online social network addiction.
This study also has important implications for
academics. In response to the call for developing
standardized instruments for diagnosing internet
dependency [10], the current study performed a
confirmatory factor analysis on [8] generalized
problematic internet use scale to test the alternative
factor structures of online social network addiction.
Our results provide a strong support for [8]’s
instrument. Finally, this study demonstrates the
advantage of using confirmatory factor analysis (CF)
for comparing alternative factor structures. CFA
facilitates researchers to define alternative models for
the testing of competing models and to generate
parameter estimates of the models. However,
indeterminacy of the hierarchical models is common
when sufficient restrictions are not imposed. This
work has been restricted to estimating only secondorder hierarchical model. Future research must
attempt to find means to estimate higher-order
structure.
8. References
[1] Wilson, S. M., & Peterson, L. C. (2002). “The
anthropology of online communities”, Annual Review of
Anthropology, 31(1), pp. 449–467.
[2] Top 25 Social Network Re-Rank. The Nielson
Company 2009.
Retrieved June 15, 2010
http://blog.compete.com/2009/02/09/facebook-myspacetwitter-social-network/
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Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
[3] Grohol, J. M. (2005). Internet addiction guide.
http://psychcentral.com/netaddiction
First-and higher order factor models and their invariance
across groups”, Psychological Bulletin, 97(3), 562–582.
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8
Proceedings of the 44th Hawaii International Conference on System Sciences - 2011
MA1
o.71( 13.25)
o.91 (17.92)
MA2
Mood
Alternation
0.95 (18.26)
MA3
SB1
SB2
0.84 (12.08)
0.72( 12.42)
o.87( 22.73)
0.857 22.77)
SB3
Social
Benefits
o.85 (21.69)
SB4
0.85 (21.88)
0.95( 19.35)
SB5
Social
Component
0.85 (11.49)
NO1
0.88 (22.62)
Negative
Outcomes
NO2
0.84 (21.26)
NO3
0.85( 17.12)
0.88 (22.55)
0.63( 11.12)
NO4
COM
P1
COM
P3
COM
P3
0.83 (11.33)
Compulsivity
0.80 (18.83)
0.69 (15.34)
0.98( 19.30)
Intrapersonal
Component
0.87 (21.47)
COM
P4
0.86 (9.95)
Excessive
Time
ET1
0.87 (22.47)
ET2
ET3
0.84( 17.02)
0.63 (13.79)
0.81 (79.93)
Withdrawal
ET4
WIT
H1
WIT
H2
0.72 (12.84)
0.84( 14.11)
0.87 (16.93)
WIT
H3
0.92 (17.69)
IC1
0.84 (9.32)
IC2
0.89 (19.98)
Interpersonal
Control
0.74( 14.19)
0.72 (15.73)
IC3
Figure 1: Factor loading and residence variances in
Model 5
9